The lnRR_func function is here used to calculate a log response ratio (lnRR) adjusted for small sample sizes. In addition, this formula accounts for correlated samples. For more details, see Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644
# packages
library(tidyverse)
library(googlesheets4)
library(here)
library(metafor)
library(metaAidR) # see a note above
library(orchaRd) # see a note above
library(ape)
library(clubSandwich)
library(metaAidR)
library(patchwork)
library(emmeans) # see a note above
library(kableExtra)
library(GGally)
library(cowplot)
library(grDevices) # reqired for using base and ggplots together
# Below is the custom function to calculate the lnRR
lnRR_func <- function(Mc, Nc, Me, Ne, aCV2c, aCV2e) {
lnRR <- log(Me/Mc) + 0.5 * ((aCV2e/Ne) - (aCV2c/Nc))
lnRR
}
# calculating lnRR's sampling variance from independent designs
var_lnRR_ind <- function(Mc, Nc, Me, Ne, aCV2c, aCV2e) {
var_lnRR <- (aCV2c/Nc) + (aCV2e/Ne)
var_lnRR
}
# calculating lnRR's sampling variance from dependent designs
var_lnRR_dep <- function(Mc, Nc, Me, Ne, aCV2c, aCV2e, rho = 0.5) {
var_lnRR <- (aCV2c/Nc) + (aCV2e/Ne) - 2 * rho * ((aCV2c * aCV2e)/sqrt(Nc * Ne))
var_lnRR
}
# Mc: Concentration of PFAS of the raw (control) sample Nc: Sample size of the
# raw (control) sample Me: Concentration of PFAS of the cooked (experimental)
# sample Ne: Sample size of the cooked (experimental) sample aCV2c: Mean
# coefficient of variation of the raw (control) samples aCV2e: Mean coefficient
# of variation of the cooked (experimental) samplesraw_data <- read_sheet("https://docs.google.com/spreadsheets/d/1cbmYDfIc2dxHJxBaowojUZZkN31NW4sL_pHw0t9eTTU/edit#gid=477880397",
range = "Data_extraction_2", skip = 1, col_types = "ccncccccncncccccnncccnccnncncnccnnncncncccccccc") # Import raw dataprocessed_data <- filter(raw_data, !PFAS_type == "PFOS_Total")
processed_data <- filter(processed_data, !Species_common == "Fish cake")
write.csv(processed_data, here("data", "Rawdata_updated_2.csv"), row.names = F)processed_data <- read.csv(here("data", "Rawdata_updated_2.csv"))
dat <- processed_data %>% mutate(SDc = ifelse(Sc_technical_biological == "biological", Sc, NA), # Calculate the SD of biological replicates for control samples
SDe = ifelse(Se_technical_biological == "biological", Se, NA)) # Calculate the SD of biological replicates for experimental samples
dat <- dat %>% filter(Species_Scientific != "?")
#### Ratio_liquid_fish with "0" for the dry cooking category
dat<-dat %>% mutate(Ratio_liquid_fish_0 = ifelse(Cooking_Category =="No liquid", 0, Ratio_liquid_fish)) # Add a 0 when the cooking category is "No liquid", otherwise keep the same value of Ratio_liquid_fish
# taking out data without species information
# arrange(select(dat, Cooking_Category, Ratio_liquid_fish, Ratio_liquid_fish_0), Cooking_Category) # Checking everything is fine
dat$Temperature_in_Celsius[dat$Temperature_in_Celsius=="?"] <- NA # Replace "?" by missing values
dat$Temperature_in_Celsius <- as.numeric(dat$Temperature_in_Celsius) # Convert integer to numeric
kable(dat, "html") %>% kable_styling("striped", position = "left") %>% scroll_box(width = "100%", height = "500px")| Study_ID | Author_year | Publication_year | Country_firstAuthor | Effect_ID | Species_common | Species_Scientific | Invertebrate_vertebrate | Fish_mollusc | Moisture_loss_in_percent | PFAS_type | PFAS_carbon_chain | linear_total | Choice_of_9 | Cooking_method | Cooking_Category | Comments_cooking | Temperature_in_Celsius | Length_cooking_time_in_s | Water | Oil | Oil_type | Volume_liquid_ml | Volume_liquid_ml_0 | Ratio_liquid_fish | Weigh_g_sample | Cohort_ID | Cohort_comment | Cohort_comment_2 | Nc | Pooled_Nc | Unit_PFAS_conc | Mc | Mc_comment | Sc | sd | Sc_technical_biological | Ne | Pooled_Ne | Me | Me_comment | Se | Se_technical_biological | If_technical_how_many | Unit_LOD_LOQ | LOD | LOQ | Design | DataSource | Raw_data_provided | General_comments | checked | SDc | SDe | Ratio_liquid_fish_0 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| F001 | Alves_2017 | 2017 | Portugal | E001 | Flounder | Platichthys flesus | vertebrate | marine fish | 7.43 | PFOS | 8 | linear | Yes | Steaming | water-based | NA | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C001 | Shared control | NA | 25.000000 | 1 | ng/g | 24.0000000 | NA | 1.5280000 | sd | technical | 25.000000 | 1 | 22.0000000 | NA | 1.53 | technical | 2 | ng/g | <0.1 | <0.2 | Dependent | Table 3 | No | Authors replied | ML - ok | NA | NA | NA |
| F001 | Alves_2017 | 2017 | Portugal | E002 | Mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Steaming | water-based | NA | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C002 | Shared control | NA | 25.000000 | 1 | ng/g | 3.1000000 | NA | 0.2120000 | sd | technical | 25.000000 | 1 | 2.9000000 | NA | 0.141 | technical | 2 | ng/g | <0.1 | <0.2 | Dependent | Table 3 | No | Authors replied | ML - ok | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E003 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.86 | PFUnDA | 11 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | Shared control | NA | 25.000000 | 1 | ng/g | 13.3018868 | NA | 0.0471698 | sd | technical | 25.000000 | 1 | 4.1510000 | NA | 0.094 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E004 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.86 | PFDoDA | 12 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | Shared control | NA | 25.000000 | 1 | ng/g | 3.5731707 | NA | 0.0243902 | sd | technical | 25.000000 | 1 | 3.2070000 | NA | 0.024 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E005 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.86 | PFTrA | 13 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | Shared control | NA | 25.000000 | 1 | ng/g | 6.5283019 | NA | 0.0754717 | sd | technical | 25.000000 | 1 | 10.0380000 | NA | 0.075 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E006 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.86 | PFTA | 14 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | Shared control | NA | 25.000000 | 1 | ng/g | 1.3736842 | NA | 0.0157895 | sd | technical | 25.000000 | 1 | 1.3320000 | NA | 0.021 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E007 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.86 | PFOS | 8 | total | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | Shared control | NA | 25.000000 | 1 | ng/g | 0.6467391 | NA | 0.0054348 | sd | technical | 25.000000 | 1 | 0.3020000 | NA | 0.008 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E008 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.86 | PFDA | 10 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | Shared control | NA | 25.000000 | 1 | ng/g | 0.0250000 | <LOQ | NA | sd | technical | 25.000000 | 1 | 0.0870000 | NA | 0.013 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E009 | European plaice | Pleuronectes platessa | vertebrate | marine fish | 8.70 | PFOS | 8 | total | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C004 | Shared control | NA | 25.000000 | 1 | ng/g | 0.2472826 | NA | 0.0081522 | sd | technical | 25.000000 | 1 | 0.2530000 | NA | 0.005 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E010 | blue mussel | Mytilus edulis | invertebrate | mollusca | 6.77 | PFBA | 3 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C005 | Shared control | NA | 50.000000 | 1 | ng/g | 0.0250000 | <LOQ | NA | sd | technical | 50.000000 | 1 | 0.2080000 | NA | 0.009 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied. We assume that the mussels were cut in half. The paper does not state this clearly but it was our assumptions that they did not use the whole mussle for each treatment. | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E011 | blue mussel | Mytilus edulis | invertebrate | mollusca | 6.77 | PFDA | 10 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C005 | Shared control | NA | 50.000000 | 1 | ng/g | 0.0241860 | NA | 0.0074419 | sd | technical | 50.000000 | 1 | 0.0250000 | <LOQ | NA | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied. We assume that the mussels were cut in half. The paper does not state this clearly but it was our assumptions that they did not use the whole mussle for each treatment. | NA | NA | NA | |
| F003 | Bhavsar_2014 | 2014 | Canada | E012 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFNA | 9 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5.000000 | 5 | 0.0860000 | NA | 0.135 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | ML | 0.0950000 | 0.135 | 0.1042160 | |
| F003 | Bhavsar_2014 | 2014 | Canada | E013 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFDA | 10 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5.000000 | 5 | 0.1920000 | NA | 0.266 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1970000 | 0.266 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E014 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFUnDA | 11 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5.000000 | 5 | 0.2340000 | NA | 0.291 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2250000 | 0.291 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E015 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFDoDA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5.000000 | 5 | 0.1010000 | NA | 0.095 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0730000 | 0.095 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E016 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFTrA | 13 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5.000000 | 5 | 0.2590000 | NA | 0.241 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1830000 | 0.241 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E017 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFTA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5.000000 | 5 | 0.0830000 | NA | 0.073 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0550000 | 0.073 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E019 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFOS | 8 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5.000000 | 5 | 16.5600000 | NA | 18 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 12.6100000 | 18 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E020 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFDS | 10 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5.000000 | 5 | 0.3970000 | NA | 0.433 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2840000 | 0.433 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E021 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | 6:6PFPIA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5.000000 | 5 | 0.0020000 | NA | 0.002 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0030000 | 0.002 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E022 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | 6:8PFPIA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5.000000 | 5 | 0.0100000 | NA | 0.016 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0230000 | 0.016 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E023 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFNA | 9 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5.000000 | 5 | 0.0830000 | NA | 0.118 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0950000 | 0.118 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E024 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFDA | 10 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5.000000 | 5 | 0.1900000 | NA | 0.232 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1970000 | 0.232 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E025 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFUnDA | 11 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5.000000 | 5 | 0.2560000 | NA | 0.31 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2250000 | 0.31 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E026 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFDoDA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5.000000 | 5 | 0.1000000 | NA | 0.08 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0730000 | 0.08 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E027 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFTrA | 13 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5.000000 | 5 | 0.2850000 | NA | 0.234 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1830000 | 0.234 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E028 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFTA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5.000000 | 5 | 0.0830000 | NA | 0.071 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0550000 | 0.071 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E030 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFOS | 8 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5.000000 | 5 | 16.4500000 | NA | 15.63 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 12.6100000 | 15.63 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E031 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFDS | 10 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5.000000 | 5 | 0.3920000 | NA | 0.359 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2840000 | 0.359 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E032 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | 6:6PFPIA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5.000000 | 5 | 0.0020000 | NA | 0.003 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0030000 | 0.003 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E033 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | 6:8PFPIA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5.000000 | 5 | 0.0140000 | NA | 0.022 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0230000 | 0.022 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E034 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFNA | 9 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5.000000 | 5 | 0.0780000 | NA | 0.114 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0950000 | 0.114 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E035 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFDA | 10 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5.000000 | 5 | 0.1820000 | NA | 0.222 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1970000 | 0.222 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E036 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFUnDA | 11 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5.000000 | 5 | 0.2270000 | NA | 0.255 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2250000 | 0.255 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E037 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFDoDA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5.000000 | 5 | 0.0960000 | NA | 0.081 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0730000 | 0.081 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E038 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFTrA | 13 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5.000000 | 5 | 0.2750000 | NA | 0.216 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1830000 | 0.216 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E039 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFTA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5.000000 | 5 | 0.0870000 | NA | 0.067 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0550000 | 0.067 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E041 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFOS | 8 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5.000000 | 5 | 16.0300000 | NA | 15.19 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 12.6100000 | 15.19 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E042 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFDS | 10 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5.000000 | 5 | 0.3930000 | NA | 0.369 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2840000 | 0.369 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E043 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | 6:6PFPIA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5.000000 | 5 | 0.0020000 | NA | 0.003 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0030000 | 0.003 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E044 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | 6:8PFPIA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5.000000 | 5 | 0.0130000 | NA | 0.022 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0230000 | 0.022 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E045 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFNA | 9 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5.000000 | 5 | 0.0990000 | NA | 0.022 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0300000 | 0.022 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E046 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFDA | 10 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5.000000 | 5 | 0.5660000 | NA | 0.138 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1070000 | 0.138 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E047 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFUnDA | 11 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5.000000 | 5 | 0.8040000 | NA | 0.167 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1580000 | 0.167 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E048 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFDoDA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5.000000 | 5 | 1.0960000 | NA | 0.396 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.3170000 | 0.396 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E049 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFTrA | 13 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5.000000 | 5 | 0.7740000 | NA | 0.332 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.4400000 | 0.332 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E050 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFTA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5.000000 | 5 | 1.1400000 | NA | 0.874 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.6470000 | 0.874 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E051 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFHxS | 6 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5.000000 | 5 | 0.3410000 | NA | 0.391 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.3190000 | 0.391 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E052 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFOS | 8 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5.000000 | 5 | 30.5200000 | NA | 9.254 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 7.7680000 | 9.254 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E053 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFDS | 10 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5.000000 | 5 | 1.0840000 | NA | 0.571 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.5320000 | 0.571 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E054 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | 6:6PFPIA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5.000000 | 5 | 0.1050000 | NA | 0.06 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0600000 | 0.06 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E055 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | 6:8PFPIA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5.000000 | 5 | 0.1800000 | NA | 0.084 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0770000 | 0.084 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E056 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFNA | 9 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5.000000 | 5 | 0.1050000 | NA | 0.037 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0300000 | 0.037 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E057 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFDA | 10 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5.000000 | 5 | 0.5480000 | NA | 0.121 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1070000 | 0.121 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E058 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFUnDA | 11 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5.000000 | 5 | 0.8480000 | NA | 0.155 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1580000 | 0.155 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E059 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFDoDA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5.000000 | 5 | 1.1080000 | NA | 0.404 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.3170000 | 0.404 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E060 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFTrA | 13 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5.000000 | 5 | 0.8280000 | NA | 0.418 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.4400000 | 0.418 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E061 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFTA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5.000000 | 5 | 1.1150000 | NA | 0.769 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.6470000 | 0.769 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E062 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFHxS | 6 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5.000000 | 5 | 0.2910000 | NA | 0.346 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.3190000 | 0.346 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E063 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFOS | 8 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5.000000 | 5 | 28.3700000 | NA | 11.99 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 7.7680000 | 11.99 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E064 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFDS | 10 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5.000000 | 5 | 1.0450000 | NA | 0.623 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.5320000 | 0.623 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E065 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | 6:6PFPIA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5.000000 | 5 | 0.1170000 | NA | 0.073 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0600000 | 0.073 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E066 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | 6:8PFPIA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5.000000 | 5 | 0.1900000 | NA | 0.08 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0770000 | 0.08 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E067 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFNA | 9 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5.000000 | 5 | 0.1010000 | NA | 0.035 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0300000 | 0.035 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E068 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFDA | 10 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5.000000 | 5 | 0.5690000 | NA | 0.108 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1070000 | 0.108 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E069 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFUnDA | 11 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5.000000 | 5 | 0.8300000 | NA | 0.13 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1580000 | 0.13 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E070 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFDoDA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5.000000 | 5 | 1.0440000 | NA | 0.356 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.3170000 | 0.356 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E071 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFTrA | 13 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5.000000 | 5 | 0.7460000 | NA | 0.283 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.4400000 | 0.283 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E072 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFTA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5.000000 | 5 | 1.0670000 | NA | 0.754 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.6470000 | 0.754 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E073 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFHxS | 6 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5.000000 | 5 | 0.3590000 | NA | 0.428 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.3190000 | 0.428 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E074 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFOS | 8 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5.000000 | 5 | 28.1100000 | NA | 10.93 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 7.7680000 | 10.93 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E075 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFDS | 10 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5.000000 | 5 | 1.0900000 | NA | 0.618 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.5320000 | 0.618 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E076 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | 6:6PFPIA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5.000000 | 5 | 0.1060000 | NA | 0.065 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0600000 | 0.065 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E077 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | 6:8PFPIA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5.000000 | 5 | 0.1880000 | NA | 0.075 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0770000 | 0.075 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E078 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFNA | 9 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4.000000 | 4 | 0.3700000 | NA | 0.189 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1430000 | 0.189 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E079 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFDA | 10 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4.000000 | 4 | 0.5100000 | NA | 0.232 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1860000 | 0.232 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E080 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFUnDA | 11 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4.000000 | 4 | 0.6850000 | NA | 0.293 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2510000 | 0.293 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E081 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFDoDA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4.000000 | 4 | 0.2210000 | NA | 0.114 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0950000 | 0.114 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E082 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFTrA | 13 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4.000000 | 4 | 0.4840000 | NA | 0.264 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2170000 | 0.264 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E083 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFTA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4.000000 | 4 | 0.1370000 | NA | 0.051 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0510000 | 0.051 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E084 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFHxS | 6 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4.000000 | 4 | 0.2480000 | NA | 0.061 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0550000 | 0.061 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E085 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFOS | 8 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4.000000 | 4 | 20.5100000 | NA | 6.752 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 6.6860000 | 6.752 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E086 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFDS | 10 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4.000000 | 4 | 0.4740000 | NA | 0.196 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1770000 | 0.196 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E087 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | 6:6PFPIA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4.000000 | 4 | 0.0020000 | NA | 0.002 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0010000 | 0.002 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E088 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | 6:8PFPIA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4.000000 | 4 | 0.0180000 | NA | 0.009 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0090000 | 0.009 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E089 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFNA | 9 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4.000000 | 4 | 0.3580000 | NA | 0.17 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1430000 | 0.17 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E090 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFDA | 10 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4.000000 | 4 | 0.5280000 | NA | 0.233 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1860000 | 0.233 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E091 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFUnDA | 11 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4.000000 | 4 | 0.7250000 | NA | 0.345 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2510000 | 0.345 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E092 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFDoDA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4.000000 | 4 | 0.2370000 | NA | 0.111 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0950000 | 0.111 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E093 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFTrA | 13 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4.000000 | 4 | 0.5580000 | NA | 0.28 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2170000 | 0.28 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E094 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFTA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4.000000 | 4 | 0.1490000 | NA | 0.068 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0510000 | 0.068 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E095 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFHxS | 6 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4.000000 | 4 | 0.2630000 | NA | 0.087 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0550000 | 0.087 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E096 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFOS | 8 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4.000000 | 4 | 22.1100000 | NA | 7.897 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 6.6860000 | 7.897 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E097 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFDS | 10 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4.000000 | 4 | 0.5600000 | NA | 0.226 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1770000 | 0.226 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E098 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | 6:6PFPIA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4.000000 | 4 | 0.0120000 | NA | 0.018 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0010000 | 0.018 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E099 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | 6:8PFPIA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4.000000 | 4 | 0.0160000 | NA | 0.006 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0090000 | 0.006 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E100 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFNA | 9 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4.000000 | 4 | 0.3740000 | NA | 0.181 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1430000 | 0.181 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E101 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFDA | 10 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4.000000 | 4 | 0.4930000 | NA | 0.207 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1860000 | 0.207 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E102 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFUnDA | 11 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4.000000 | 4 | 0.6830000 | NA | 0.286 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2510000 | 0.286 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E103 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFDoDA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4.000000 | 4 | 0.2320000 | NA | 0.103 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0950000 | 0.103 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E104 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFTrA | 13 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4.000000 | 4 | 0.5190000 | NA | 0.212 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2170000 | 0.212 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E105 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFTA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4.000000 | 4 | 0.1290000 | NA | 0.045 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0510000 | 0.045 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E106 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFHxS | 6 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4.000000 | 4 | 0.2450000 | NA | 0.077 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0550000 | 0.077 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E107 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFOS | 8 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4.000000 | 4 | 21.6700000 | NA | 8.008 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 6.6860000 | 8.008 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E108 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFDS | 10 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4.000000 | 4 | 0.5160000 | NA | 0.244 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1770000 | 0.244 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E109 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | 6:6PFPIA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4.000000 | 4 | 0.0020000 | NA | 0.001 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0010000 | 0.001 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E110 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | 6:8PFPIA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4.000000 | 4 | 0.0160000 | NA | 0.006 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0090000 | 0.006 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E111 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFNA | 9 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5.000000 | 5 | 0.0790000 | NA | 0.023 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0210000 | 0.023 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E112 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFDA | 10 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5.000000 | 5 | 0.3490000 | NA | 0.094 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0400000 | 0.094 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E113 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFUnDA | 11 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5.000000 | 5 | 0.3330000 | NA | 0.091 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0400000 | 0.091 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E114 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFDoDA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5.000000 | 5 | 0.1330000 | NA | 0.012 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0190000 | 0.012 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E115 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFTrA | 13 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5.000000 | 5 | 0.1800000 | NA | 0.021 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0200000 | 0.021 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E116 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFTA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5.000000 | 5 | 0.0930000 | NA | 0.023 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0090000 | 0.023 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E117 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFHxS | 6 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5.000000 | 5 | 0.0980000 | NA | 0.034 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0250000 | 0.034 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E118 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFOS | 8 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5.000000 | 5 | 45.0900000 | NA | 3.709 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 1.6240000 | 3.709 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E119 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFDS | 10 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5.000000 | 5 | 0.1780000 | NA | 0.094 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0240000 | 0.094 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E120 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | 6:6PFPIA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5.000000 | 5 | 0.0350000 | NA | 0.006 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0060000 | 0.006 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E121 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | 6:8PFPIA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5.000000 | 5 | 0.0630000 | NA | 0.017 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0100000 | 0.017 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E122 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFNA | 9 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5.000000 | 5 | 0.0740000 | NA | 0.014 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0210000 | 0.014 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E123 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFDA | 10 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5.000000 | 5 | 0.3380000 | NA | 0.098 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0400000 | 0.098 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E124 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFUnDA | 11 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5.000000 | 5 | 0.3480000 | NA | 0.102 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0400000 | 0.102 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E125 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFDoDA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5.000000 | 5 | 0.1440000 | NA | 0.037 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0190000 | 0.037 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E126 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFTrA | 13 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5.000000 | 5 | 0.2170000 | NA | 0.041 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0200000 | 0.041 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E127 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFTA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5.000000 | 5 | 0.0940000 | NA | 0.025 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0090000 | 0.025 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E128 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFHxS | 6 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5.000000 | 5 | 0.0880000 | NA | 0.036 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0250000 | 0.036 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E129 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFOS | 8 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5.000000 | 5 | 52.6900000 | NA | 14.62 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 1.6240000 | 14.62 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E130 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFDS | 10 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5.000000 | 5 | 0.1890000 | NA | 0.08 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0240000 | 0.08 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E131 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | 6:6PFPIA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5.000000 | 5 | 0.0400000 | NA | 0.008 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0060000 | 0.008 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E132 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | 6:8PFPIA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5.000000 | 5 | 0.0870000 | NA | 0.012 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0100000 | 0.012 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E133 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFNA | 9 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5.000000 | 5 | 0.0670000 | NA | 0.015 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0210000 | 0.015 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E134 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFDA | 10 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5.000000 | 5 | 0.2990000 | NA | 0.072 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0400000 | 0.072 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E135 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFUnDA | 11 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5.000000 | 5 | 0.3070000 | NA | 0.076 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0400000 | 0.076 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E136 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFDoDA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5.000000 | 5 | 0.1290000 | NA | 0.049 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0190000 | 0.049 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E137 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFTrA | 13 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5.000000 | 5 | 0.1790000 | NA | 0.054 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0200000 | 0.054 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E138 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFTA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5.000000 | 5 | 0.0870000 | NA | 0.034 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0090000 | 0.034 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E139 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFHxS | 6 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5.000000 | 5 | 0.0830000 | NA | 0.027 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0250000 | 0.027 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E140 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFOS | 8 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5.000000 | 5 | 44.5100000 | NA | 7.718 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 1.6240000 | 7.718 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E141 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFDS | 10 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5.000000 | 5 | 0.1570000 | NA | 0.066 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0240000 | 0.066 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E142 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | 6:6PFPIA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5.000000 | 5 | 0.0290000 | NA | 0.004 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0060000 | 0.004 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E143 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | 6:8PFPIA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5.000000 | 5 | 0.0770000 | NA | 0.005 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0100000 | 0.005 | 0.0615832 | ||
| F005 | DelGobbo_2008 | 2008 | Canada | E144 | Catfish | Ictalurus punctatus | vertebrate | freshwater fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C017 | Shared control | NA | 19.000000 | 1 | ng/g | 1.5657252 | NA | NA | Not available because sample size is one. | technical | 19.000000 | 1 | 0.9000000 | NA | Not provided | technical | 4 | ng/g | 0.364605839 | 1.093817517 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | ML | NA | NA | 0.0625000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E145 | Grouper | Epinephelus itajara | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C018 | Shared control | NA | 14.000000 | 1 | ng/g | 1.3600000 | NA | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 0.0169896 | LOD | Not provided | technical | 4 | ng/g | 0.016989626 | 0.050968879 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 0.0625000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E146 | Grouper | Epinephelus itajara | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C018 | Shared control | NA | 14.000000 | 1 | ng/g | 0.3715856 | LOD | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 0.4700000 | NA | Not provided | technical | 4 | ng/g | 0.371585648 | 1.114756944 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 0.0625000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E147 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | Shared control | NA | 9.000000 | 1 | ng/g | 0.0774969 | LOD | NA | Not available because sample size is one. | technical | 9.000000 | 1 | 0.0600000 | NA | Not provided | technical | 4 | ng/g | 0.077496939 | 0.232490816 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E148 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | Shared control | NA | 9.000000 | 1 | ng/g | 1.3400000 | NA | NA | Not available because sample size is one. | technical | 9.000000 | 1 | 0.0032120 | LOD | Not provided | technical | 4 | ng/g | 0.003212028 | 0.009636084 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E149 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | Shared control | NA | 9.000000 | 1 | ng/g | 0.0270203 | LOD | NA | Not available because sample size is one. | technical | 9.000000 | 1 | 0.3900000 | NA | Not provided | technical | 4 | ng/g | 0.027020324 | 0.081060971 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E150 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | Shared control | NA | 9.000000 | 1 | ng/g | 1.3400000 | NA | NA | Not available because sample size is one. | technical | 9.000000 | 1 | 0.2200000 | NA | Not provided | technical | 4 | ng/g | 0.233373227 | 0.70011968 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E151 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | Shared control | NA | 15.000000 | 1 | ng/g | 0.7800000 | NA | NA | Not available because sample size is one. | technical | 15.000000 | 1 | 0.0600000 | NA | Not provided | technical | 3 | ng/g | 0.026125853 | 0.07837756 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E152 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | Shared control | NA | 15.000000 | 1 | ng/g | 1.2900000 | NA | NA | Not available because sample size is one. | technical | 15.000000 | 1 | 0.0261259 | LOD | Not provided | technical | 3 | ng/g | 0.026125853 | 0.07837756 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E153 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFDA | 10 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | Shared control | NA | 15.000000 | 1 | ng/g | 1.5500000 | NA | NA | Not available because sample size is one. | technical | 15.000000 | 1 | 0.0120876 | LOD | Not provided | technical | 3 | ng/g | 0.012087592 | 0.036262776 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E154 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | Shared control | NA | 15.000000 | 1 | ng/g | 1.8800000 | NA | NA | Not available because sample size is one. | technical | 15.000000 | 1 | 1.5900000 | NA | Not provided | technical | 3 | ng/g | 0.023403463 | 0.07021039 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E155 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFTA | 14 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | Shared control | NA | 15.000000 | 1 | ng/g | 2.6100000 | NA | NA | Not available because sample size is one. | technical | 15.000000 | 1 | 0.0071943 | LOD | Not provided | technical | 3 | ng/g | 0.007194278 | 0.021582834 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E156 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | Shared control | NA | 15.000000 | 1 | ng/g | 0.5086163 | LOD | NA | Not available because sample size is one. | technical | 15.000000 | 1 | 0.2300000 | NA | Not provided | technical | 3 | ng/g | 0.508616305 | 1.525848915 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E157 | Red snapper | Lutjanus campechanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C021 | Shared control | NA | 19.000000 | 1 | ng/g | 1.4600000 | NA | NA | Not available because sample size is one. | technical | 19.000000 | 1 | 0.2100000 | NA | Not provided | technical | 4 | ng/g | 0.335745729 | 1.007237187 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E158 | Red snapper | Lutjanus campechanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C021 | Shared control | NA | 19.000000 | 1 | ng/g | 1.4600000 | NA | NA | Not available because sample size is one. | technical | 19.000000 | 1 | 0.7800000 | NA | Not provided | technical | 4 | ng/g | 0.212707733 | 0.6381232 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 0.0625000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E159 | Sea squirt | Diplosoma listerianum | vertebrate | tunicata | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C022 | Shared control | NA | 22.000000 | 1 | ng/g | 1.5800000 | NA | NA | Not available because sample size is one. | technical | 22.000000 | 1 | 1.5900000 | NA | Not provided | technical | 3 | ng/g | 0.030799263 | 0.092397789 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E160 | Sea squirt | Diplosoma listerianum | vertebrate | tunicata | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C022 | Shared control | NA | 22.000000 | 1 | ng/g | 1.3200000 | NA | NA | Not available because sample size is one. | technical | 22.000000 | 1 | 0.9600000 | NA | Not provided | technical | 3 | ng/g | 0.00466163 | 0.013984889 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E161 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | Shared control | NA | 14.000000 | 1 | ng/g | 1.0900000 | NA | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 0.0027709 | LOD | Not provided | technical | 4 | ng/g | 0.002770915 | 0.008312745 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E162 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | Shared control | NA | 14.000000 | 1 | ng/g | 1.5500000 | NA | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 1.3500000 | NA | Not provided | technical | 4 | ng/g | 0.012033653 | 0.03610096 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E163 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFDoDA | 12 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | Shared control | NA | 14.000000 | 1 | ng/g | 1.3300000 | NA | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 0.0255728 | LOD | Not provided | technical | 4 | ng/g | 0.025572815 | 0.076718446 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E164 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFTA | 14 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | Shared control | NA | 14.000000 | 1 | ng/g | 0.6700000 | NA | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 0.0070174 | LOD | Not provided | technical | 4 | ng/g | 0.00701744 | 0.021052319 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E165 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | Shared control | NA | 14.000000 | 1 | ng/g | 1.5100000 | NA | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 0.8800000 | NA | Not provided | technical | 4 | ng/g | 0.364216663 | 1.092649988 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E166 | Yellow croaker | Larimichthys polyactis | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C024 | Shared control | NA | 35.000000 | 1 | ng/g | 1.5700000 | NA | NA | Not available because sample size is one. | technical | 35.000000 | 1 | 0.0179042 | LOD | Not provided | technical | 4 | ng/g | 0.017904207 | 0.05371262 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E167 | Yellow croaker | Larimichthys polyactis | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C024 | Shared control | NA | 35.000000 | 1 | ng/g | 1.6800000 | NA | NA | Not available because sample size is one. | technical | 35.000000 | 1 | 0.8900000 | NA | Not provided | technical | 4 | ng/g | 0.376885418 | 1.130656253 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E168 | Yellow croaker | Larimichthys polyactis | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C025 | Shared control | NA | 35.000000 | 1 | ng/g | 1.5700000 | NA | NA | Not available because sample size is one. | technical | 35.000000 | 1 | 2.1100000 | NA | Not provided | technical | 4 | ng/g | 0.016586028 | 0.049758083 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 0.0625000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E169 | Yellow croaker | Larimichthys polyactis | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C025 | Shared control | NA | 35.000000 | 1 | ng/g | 1.6800000 | NA | NA | Not available because sample size is one. | technical | 35.000000 | 1 | 0.6800000 | NA | Not provided | technical | 4 | ng/g | 0.392175529 | 1.176526586 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 0.0625000 | |
| F006 | Hu_2020 | 2020 | China | E170 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFBA | 3 | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5.000000 | 5 | 5.3412073 | NA | 1.688925302 | biological | Not specified | ng/g | Not provided | 12.2 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | ML | 7.4193907 | 1.688925302 | NA | |
| F006 | Hu_2020 | 2020 | China | E171 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFOA | 8 | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5.000000 | 5 | 0.2674068 | NA | 0.08 | biological | Not specified | ng/g | Not provided | 0.226 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.1560332 | 0.08 | NA | ||
| F006 | Hu_2020 | 2020 | China | E172 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFBS | 4 | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5.000000 | 5 | 23.9801208 | NA | 26.845 | biological | Not specified | ng/g | Not provided | 1.01 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 23.9889753 | 26.845 | NA | ||
| F006 | Hu_2020 | 2020 | China | E173 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFOS | 8 | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5.000000 | 5 | 122.4133110 | NA | 62.469 | biological | Not specified | ng/g | Not provided | 1.57 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 39.4592027 | 62.469 | NA | ||
| F006 | Hu_2020 | 2020 | China | E174 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFHpA | 7 | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5.000000 | 5 | 55.3995680 | NA | 55.4 | biological | Not specified | ng/g | Not provided | 0.47 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 30.6129835 | 55.4 | NA | ||
| F006 | Hu_2020 | 2020 | China | E175 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFDoDA | 12 | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5.000000 | 5 | 2.2676991 | NA | 1.533 | biological | Not specified | ng/g | Not provided | 0.093 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.5599538 | 1.533 | NA | ||
| F006 | Hu_2020 | 2020 | China | E176 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFHxS | 6 | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5.000000 | 5 | 0.8685897 | NA | 0.303 | biological | Not specified | ng/g | Not provided | 0.155 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 2.3827419 | 0.303 | NA | ||
| F006 | Hu_2020 | 2020 | China | E177 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | FOSA | 8 | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5.000000 | 5 | 2.3838798 | NA | 1.290418258 | biological | Not specified | ng/g | Not provided | 0.026 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 1.6889253 | 1.290418258 | NA | ||
| F006 | Hu_2020 | 2020 | China | E178 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFBA | 3 | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5.000000 | 5 | 4.9146982 | NA | 7.434 | biological | Not specified | ng/g | Not provided | 12.2 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 7.4193907 | 7.434 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E179 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFOA | 8 | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5.000000 | 5 | 0.1932566 | NA | 0.071 | biological | Not specified | ng/g | Not provided | 0.226 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.1560332 | 0.071 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E180 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFBS | 4 | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5.000000 | 5 | 10.8230680 | NA | 7.461 | biological | Not specified | ng/g | Not provided | 1.01 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 23.9889753 | 7.461 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E181 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFOS | 8 | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5.000000 | 5 | 97.7348993 | NA | 23.173 | biological | Not specified | ng/g | Not provided | 1.57 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 39.4592027 | 23.173 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E182 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFHpA | 7 | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5.000000 | 5 | 13.7149028 | NA | 23.604 | biological | Not specified | ng/g | Not provided | 0.47 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 30.6129835 | 23.604 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E183 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFDoDA | 12 | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5.000000 | 5 | 2.3534292 | NA | 2.484 | biological | Not specified | ng/g | Not provided | 0.093 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.5599538 | 2.484 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E184 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFHxS | 6 | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5.000000 | 5 | 0.6506410 | NA | 0.108 | biological | Not specified | ng/g | Not provided | 0.155 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 2.3827419 | 0.108 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E185 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | FOSA | 8 | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5.000000 | 5 | 2.2540984 | NA | 1.248 | biological | Not specified | ng/g | Not provided | 0.026 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 1.6889253 | 1.248 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E186 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFBA | 3 | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5.000000 | 5 | 7.9068241 | NA | 9.381 | biological | Not specified | ng/g | Not provided | 12.2 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 7.4193907 | 9.381 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E187 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFOA | 8 | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5.000000 | 5 | 0.2308114 | NA | 0.154 | biological | Not specified | ng/g | Not provided | 0.226 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.1560332 | 0.154 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E188 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFBS | 4 | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5.000000 | 5 | 9.8657220 | NA | 5.801 | biological | Not specified | ng/g | Not provided | 1.01 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 23.9889753 | 5.801 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E189 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFOS | 8 | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5.000000 | 5 | 134.4379195 | NA | 58.054 | biological | Not specified | ng/g | Not provided | 1.57 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 39.4592027 | 58.054 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E190 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFHpA | 7 | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5.000000 | 5 | 23.7041037 | NA | 35.93 | biological | Not specified | ng/g | Not provided | 0.47 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 30.6129835 | 35.93 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E191 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFDoDA | 12 | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5.000000 | 5 | 2.8733407 | NA | 2.747 | biological | Not specified | ng/g | Not provided | 0.093 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.5599538 | 2.747 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E192 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFHxS | 6 | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5.000000 | 5 | 1.1602564 | NA | 0.738 | biological | Not specified | ng/g | Not provided | 0.155 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 2.3827419 | 0.738 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E193 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | FOSA | 8 | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5.000000 | 5 | 3.7500000 | NA | 3.741 | biological | Not specified | ng/g | Not provided | 0.026 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 1.6889253 | 3.741 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E194 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFBA | 3 | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5.000000 | 5 | 4.8490814 | NA | 6.93 | biological | Not specified | ng/g | Not provided | 12.2 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 7.4193907 | 6.93 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E195 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFOA | 8 | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5.000000 | 5 | 0.1652961 | NA | 0.063 | biological | Not specified | ng/g | Not provided | 0.226 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.1560332 | 0.063 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E196 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFBS | 4 | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5.000000 | 5 | 7.5376305 | NA | 1.502 | biological | Not specified | ng/g | Not provided | 1.01 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 23.9889753 | 1.502 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E197 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFOS | 8 | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5.000000 | 5 | 121.7142058 | NA | 62.557 | biological | Not specified | ng/g | Not provided | 1.57 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 39.4592027 | 62.557 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E198 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFHpA | 7 | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5.000000 | 5 | 10.0971922 | NA | 16.49 | biological | Not specified | ng/g | Not provided | 0.47 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 30.6129835 | 16.49 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E199 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFDoDA | 12 | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5.000000 | 5 | 2.9120575 | NA | 3.36 | biological | Not specified | ng/g | Not provided | 0.093 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.5599538 | 3.36 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E200 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFHxS | 6 | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5.000000 | 5 | 0.8253205 | NA | 0.254 | biological | Not specified | ng/g | Not provided | 0.155 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 2.3827419 | 0.254 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E201 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | FOSA | 8 | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5.000000 | 5 | 2.2814208 | NA | 0.43 | biological | Not specified | ng/g | Not provided | 0.026 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 1.6889253 | 0.43 | 0.1428571 | ||
| F007 | Kim_2020 | 2020 | Korea | E202 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | Yes | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. For volume of cooking liquid: 1 cup is 250 ml, accordingly for table spoon etc. | ML | NA | NA | 0.0500000 | |
| F007 | Kim_2020 | 2020 | Korea | E203 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | Yes | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.1100000 | NA | 0 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E204 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | Yes | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | NA | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E205 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | Yes | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0900000 | NA | 0.06 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 7.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E206 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 0.0500000 | ||
| F007 | Kim_2020 | 2020 | Korea | E207 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.1300000 | NA | 0.04 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E208 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.1400000 | NA | 0.01 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E209 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0900000 | NA | 0 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 7.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E210 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 0.0500000 | ||
| F007 | Kim_2020 | 2020 | Korea | E211 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E212 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E213 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 7.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E214 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 0.0500000 | ||
| F007 | Kim_2020 | 2020 | Korea | E215 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E216 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E217 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 7.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E218 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0500000 | NA | 0 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 0.0500000 | ||
| F007 | Kim_2020 | 2020 | Korea | E219 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0600000 | NA | 0.02 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E220 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E221 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0600000 | NA | 0 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 7.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E222 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | Yes | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 0.0500000 | ||
| F007 | Kim_2020 | 2020 | Korea | E223 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | Yes | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E224 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | Yes | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E225 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | Yes | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 7.5000000 | ||
| F008 | Luo_2019 | 2019 | Korea | E316 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFOA | 8 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 20.7900000 | NA | 0.1700000 | sd | technical | 5.000000 | 1 | 16.7700000 | NA | 0.42 | technical | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | Scientific name of swimming crab not provided in paper, inferred as this species of swimming crab is commonly eaten in South korea (Kim, S., Lee, M.J., Lee, J.J., Choi, S.H. and Kim, B.S., 2017. Analysis of microbiota of the swimming crab (Portunus trituberculatus) in South Korea to identify risk markers for foodborne illness. LWT, 86, pp.483-491.) | NA | NA | 2.5000000 | |||
| F008 | Luo_2019 | 2019 | Korea | E317 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFOS | 8 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.8100000 | NA | 0.0200000 | sd | technical | 5.000000 | 1 | 0.7400000 | NA | 0.03 | technical | ng/g | 0.07 | 0.07 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E318 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFBA | 3 | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.1400000 | NA | 0.0100000 | sd | technical | 5.000000 | 1 | 0.0400000 | NA | 0.01 | technical | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E319 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFHpA | 7 | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.3700000 | NA | 0.0300000 | sd | technical | 5.000000 | 1 | 0.3200000 | NA | 0.01 | technical | ng/g | 0.06 | 0.17 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E320 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFNA | 9 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 2.8900000 | NA | 0.0200000 | sd | technical | 5.000000 | 1 | 2.3000000 | NA | 0.03 | technical | ng/g | 0.03 | 0.08 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E321 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDA | 10 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.6600000 | NA | 0.0200000 | sd | technical | 5.000000 | 1 | 0.5700000 | NA | 0.02 | technical | ng/g | 0.04 | 0.11 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E322 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFUnDA | 11 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.9300000 | NA | 0.0100000 | sd | technical | 5.000000 | 1 | 0.7900000 | NA | 0.02 | technical | ng/g | 0.08 | 0.25 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E323 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDoDA | 12 | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.2500000 | NA | 0.0200000 | sd | technical | 5.000000 | 1 | 0.2300000 | NA | 0.01 | technical | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E324 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFTrA | 13 | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 1.1200000 | NA | 0.0600000 | sd | technical | 5.000000 | 1 | 1.3800000 | NA | 0.09 | technical | ng/g | 0.05 | 0.16 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E325 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFTA | 14 | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.2800000 | NA | 0.0100000 | sd | technical | 5.000000 | 1 | 0.2600000 | NA | 0.02 | technical | ng/g | 0.05 | 0.15 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E326 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFHxS | 6 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.4800000 | NA | 0.0300000 | sd | technical | 5.000000 | 1 | 0.3300000 | NA | 0.03 | technical | ng/g | 0.08 | 0.25 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E327 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDS | 10 | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.0400000 | NA | 0.0100000 | sd | technical | 5.000000 | 1 | 0.0400000 | NA | 0.01 | technical | ng/g | 0.09 | 0.27 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E328 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | FOSA | 8 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 1.5400000 | NA | 0.0900000 | sd | technical | 5.000000 | 1 | 2.5500000 | NA | 0.19 | technical | ng/g | 0.04 | 0.11 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F010 | Sungur_2019 | 2019 | Turkey | E329 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C041 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1590000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | ML - note shared controls for differend cooking times and methods | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E330 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C042 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1170000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E331 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C043 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0790000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E332 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C044 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1420000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E333 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C045 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1160000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E334 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C046 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0980000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E335 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C047 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1400000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E336 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C048 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1330000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E337 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C049 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0710000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E338 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C050 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2010000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E339 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C051 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0590000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E340 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C052 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0480000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E341 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C041 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 14.7000000 | NA | 0.009 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E342 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C042 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 9.3500000 | NA | 0.008 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E343 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C043 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 3.6600000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E344 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C044 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 5.6300000 | NA | 0.005 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E345 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C045 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 4.5000000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E346 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C046 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 3.7700000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E347 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C047 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 8.2800000 | NA | 0.007 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E348 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C048 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 6.6200000 | NA | 0.006 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E349 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C049 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 3.4800000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E350 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C050 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 4.4900000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E351 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C051 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 3.0500000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E352 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C052 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 2.8300000 | NA | 0.003 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E353 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C053 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1960000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E354 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C054 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1180000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E355 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C055 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0840000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E356 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C056 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2030000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E357 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C057 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1390000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E358 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C058 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1040000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E359 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C059 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2070000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E360 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C060 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0970000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E361 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C061 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0820000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E362 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C062 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1960000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E363 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C063 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0510000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E364 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C064 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2550000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E365 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C053 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 4.7800000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E366 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C054 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 3.5000000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E367 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C055 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 1.5100000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E368 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C056 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 7.0500000 | NA | 0.006 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E369 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C057 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 2.4700000 | NA | 0.003 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E370 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C058 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 1.7600000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E371 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C059 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 3.0300000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E372 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C060 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 2.0400000 | NA | 0.003 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E373 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C061 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 1.2300000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E374 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C062 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 4.2800000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E375 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C063 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 2.7800000 | NA | 0.003 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E376 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C064 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 1.0200000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E377 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C065 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2420000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E378 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C066 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1870000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E379 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C067 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0960000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E380 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C068 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1750000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E381 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C069 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1530000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E382 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C070 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0980000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E383 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C071 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1890000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E384 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C072 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1320000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E385 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C073 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0930000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E386 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C074 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1810000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E387 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C075 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0880000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E388 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C076 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0660000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E389 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C065 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 4.1500000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E390 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C066 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 2.6500000 | NA | 0.003 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E391 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C067 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 1.2300000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E392 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C068 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 4.4400000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E393 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C069 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 2.3600000 | NA | 0.003 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E394 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C070 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 1.6500000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E395 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C071 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 3.6800000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E396 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C072 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 1.7300000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E397 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C073 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 0.9200000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E398 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C074 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 4.0300000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E399 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C075 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 1.9700000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E400 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C076 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 0.8400000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E401 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C077 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2020000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E402 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C078 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1280000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E403 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C079 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0920000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E404 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C080 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1580000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E405 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C081 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1210000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E406 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C082 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0980000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E407 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C083 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1680000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E408 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C084 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1340000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E409 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C085 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0910000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E410 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C086 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1740000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E411 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C087 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0960000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E412 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C088 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0440000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E413 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C077 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2760000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E414 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C078 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1750000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E415 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C079 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0900000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E416 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C080 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3110000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E417 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C081 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2840000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E418 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C082 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0940000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E419 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C083 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2970000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E420 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C084 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1610000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E421 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C085 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0850000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E422 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C086 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1640000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E423 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C087 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0930000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E424 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C088 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0670000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E425 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C089 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1970000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E426 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C090 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1460000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E427 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C091 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0900000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E428 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C092 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2120000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E429 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C093 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1220000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E430 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C094 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0940000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E431 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C095 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1470000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E432 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C096 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1280000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E433 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C097 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0690000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E434 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C098 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1450000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E435 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C099 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1020000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E436 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C100 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0420000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E437 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C089 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3720000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E438 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C090 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2510000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E439 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C091 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0940000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E440 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C092 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2540000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E441 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C093 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1800000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E442 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C094 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0970000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E443 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C095 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3260000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E444 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C096 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1550000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E445 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C097 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0630000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E446 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C098 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3580000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E447 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C099 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1970000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E448 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C100 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0560000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E449 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C101 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1470000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E450 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C102 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1150000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E451 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C103 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0500000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E452 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C104 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1480000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E453 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C105 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1070000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E454 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | oil-based | NA | 160 | 1200 | No | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C106 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0570000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E455 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C107 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1210000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E456 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C108 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0950000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E457 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C109 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0430000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E458 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C110 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1150000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E459 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C111 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0820000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E460 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C112 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0330000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E461 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C101 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.6640000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E462 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C102 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3120000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E463 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C103 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0990000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E464 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C104 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.6180000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E465 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C105 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3780000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E466 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C106 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1070000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E467 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C107 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.5980000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E468 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C108 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.4020000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E469 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C109 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0970000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E470 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C110 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.6180000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E471 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C111 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2460000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E472 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C112 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0890000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E473 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C113 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0980000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E474 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C114 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0620000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E475 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C115 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0430000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E476 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C116 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0800000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E477 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C117 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0600000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E478 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C118 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0450000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E479 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C119 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0980000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E480 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C120 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0700000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E481 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C121 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0340000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E482 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C122 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0650000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E483 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C123 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0580000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E484 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C124 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0320000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E485 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C113 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1540000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E486 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C114 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1080000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E487 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C115 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0920000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E488 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C116 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1470000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E489 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C117 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1020000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E490 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C118 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0940000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E491 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C119 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1260000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E492 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C120 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0990000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E493 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C121 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0520000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E494 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C122 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1020000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E495 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C123 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0760000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E496 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C124 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0490000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E497 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C125 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1450000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E498 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C126 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1130000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E499 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C127 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0540000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E500 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C128 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1520000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E501 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C129 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1280000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E502 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C130 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0610000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E503 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C131 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1220000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E504 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C132 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0920000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E505 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C133 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0490000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E506 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C134 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1180000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E507 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C135 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0890000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E508 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C136 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0440000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E509 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C125 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3570000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E510 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C126 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2100000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E511 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C127 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0920000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E512 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C128 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2560000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E513 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C129 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1840000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E514 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C130 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0990000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E515 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C131 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3440000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E516 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C132 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1480000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E517 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C133 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0820000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E518 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C134 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3410000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E519 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C135 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1920000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E520 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C136 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0540000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F011 | Taylor_2019 | 2019 | Australia | E521 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.47 | PFHxS | 6 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Shared control | Contaminated site | 4.000000 | 4 | ng/g | 0.9673000 | NA | 1.0026000 | sd | biological | 4.000000 | 4 | 1.4750000 | NA | 1.743 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | ML - check empty fields, why SE/SD field is NA? | 1.0026000 | 1.743 | 0.3976934 |
| F011 | Taylor_2019 | 2019 | Australia | E522 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.47 | PFOS | 8 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Shared control | Contaminated site | 6.000000 | 6 | ng/g | 75.6360000 | NA | 133.7000000 | sd | biological | 6.000000 | 6 | 84.5500000 | NA | 130.5 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 133.7000000 | 130.5 | 0.3976934 | |
| F011 | Taylor_2019 | 2019 | Australia | E523 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.47 | PFOS | 8 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.4610420 | 43.3800 | C138 | Shared control | Clean site | 3.000000 | 3 | ng/g | 0.0894000 | NA | 0.0339000 | sd | biological | 3.000000 | 3 | 0.1210000 | NA | 0.039 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0339000 | 0.039 | 0.4610420 | |
| F011 | Taylor_2019 | 2019 | Australia | E524 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.47 | PFOS_Total | 8 | total | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Shared control | Contaminated site | 6.000000 | 6 | ng/g | 83.4760000 | NA | 141.7900000 | sd | biological | 6.000000 | 6 | 106.5080000 | NA | 164.5 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 141.7900000 | 164.5 | 0.3976934 | |
| F011 | Taylor_2019 | 2019 | Australia | E525 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.47 | PFOS_Total | 8 | total | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.4610420 | 43.3800 | C138 | Shared control | Clean site | 5.000000 | 5 | ng/g | 0.0969000 | NA | 0.0349000 | sd | biological | 5.000000 | 5 | 0.1100000 | NA | 0.031 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0349000 | 0.031 | 0.4610420 | |
| F011 | Taylor_2019 | 2019 | Australia | E526 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.47 | PFDS | 10 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Shared control | Contaminated site | 2.000000 | 2 | ng/g | 0.1391000 | NA | 0.0247000 | sd | biological | 2.000000 | 2 | 0.3760000 | NA | 0.024 | biological | 1 | ng/g | 0.030122517 | 0.10040839 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0247000 | 0.024 | 0.3976934 | |
| F011 | Taylor_2019 | 2019 | Australia | E527 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.47 | FOSA | 8 | NA | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Shared control | Contaminated site | 2.000000 | 2 | ng/g | 0.0749000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 2.000000 | 2 | 0.1990000 | NA | 0.012 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.012 | 0.3976934 | |
| F011 | Taylor_2019 | 2019 | Australia | E528 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.64 | PFHxS | 6 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C140 | Shared control | Contaminated site | 5.000000 | 5 | ng/g | 0.7841000 | NA | 0.9602000 | sd | biological | 5.000000 | 5 | 0.8410000 | NA | 1.042 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.9602000 | 1.042 | 0.7696748 | |
| F011 | Taylor_2019 | 2019 | Australia | E529 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.64 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C139 | Shared control | Contaminated site | 6.000000 | 6 | ng/g | 75.6360000 | NA | 133.7000000 | sd | biological | 6.000000 | 6 | 70.8430000 | NA | 106 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 133.7000000 | 106 | 0.7696748 | |
| F011 | Taylor_2019 | 2019 | Australia | E530 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.64 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.9220839 | 43.3800 | C140 | Shared control | Clean site | 2.000000 | 2 | ng/g | 0.1090000 | NA | 0.0014000 | sd | biological | 2.000000 | 2 | 0.2010000 | NA | 0.073 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0014000 | 0.073 | 0.9220839 | |
| F011 | Taylor_2019 | 2019 | Australia | E531 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.64 | PFOS_Total | 8 | total | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C139 | Shared control | Contaminated site | 6.000000 | 6 | ng/g | 83.4760000 | NA | 141.7900000 | sd | biological | 6.000000 | 6 | 87.2460000 | NA | 130.3 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 141.7900000 | 130.3 | 0.7696748 | |
| F011 | Taylor_2019 | 2019 | Australia | E532 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.64 | PFOS_Total | 8 | total | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.9220839 | 43.3800 | C140 | Shared control | Clean site | 4.000000 | 4 | ng/g | 0.1086000 | NA | 0.0268000 | sd | biological | 4.000000 | 4 | 0.1520000 | NA | 0.118 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0268000 | 0.118 | 0.9220839 | |
| F011 | Taylor_2019 | 2019 | Australia | E533 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.64 | FOSA | 8 | NA | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C139 | Shared control | Contaminated site | 4.000000 | 4 | ng/g | 0.1070000 | NA | 0.0397000 | sd | biological | 4.000000 | 4 | 0.2540000 | NA | 0.132 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0397000 | 0.132 | 0.7696748 | |
| F011 | Taylor_2019 | 2019 | Australia | E534 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxA | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 3.000000 | 3 | ng/g | 0.1513000 | NA | 0.0306000 | sd | biological | 3.000000 | 3 | 0.0730000 | NA | 0.021 | biological | 1 | ng/g | 0.028099467 | 0.093664888 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0306000 | 0.021 | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E535 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHpA | 7 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 6.000000 | 6 | ng/g | 0.2070000 | NA | 0.1445000 | sd | biological | 6.000000 | 6 | 0.1090000 | NA | 0.052 | biological | 1 | ng/g | 0.01867491 | 0.0622497 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.1445000 | 0.052 | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E536 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 6.000000 | 6 | ng/g | 0.4279000 | NA | 0.2601000 | sd | biological | 6.000000 | 6 | 0.2320000 | NA | 0.107 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.2601000 | 0.107 | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E537 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | NA | Clean site | 4.000000 | 4 | ng/g | 0.0433000 | NA | 0.0137000 | sd | biological | 4.000000 | 4 | 0.0710000 | NA | 0.066 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0137000 | 0.066 | 40.4858300 | |
| F011 | Taylor_2019 | 2019 | Australia | E538 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFUnDA | 11 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 4.000000 | 4 | ng/g | 0.1128000 | NA | 0.0093000 | sd | biological | 4.000000 | 4 | 0.0580000 | <LOQ | NA | NA | 1 | ng/g | 0.026755217 | 0.089184057 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0093000 | NA | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E539 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFUnDA | 11 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | NA | Clean site | 1.000000 | 1 | ng/g | 0.1047000 | NA | NA | sd | biological | 1.000000 | 1 | 0.0580000 | <LOQ | NA | NA | 1 | ng/g | 0.026755217 | 0.089184057 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 40.4858300 | |
| F011 | Taylor_2019 | 2019 | Australia | E540 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 1.000000 | 1 | ng/g | 0.0802000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 1.000000 | 1 | 0.1280000 | No sd, as N = 1 | NA | NA | 1 | ng/g | 0.037026547 | 0.123421824 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E541 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | NA | Clean site | 1.000000 | 1 | ng/g | 0.1230000 | NA | NA | sd | biological | 1.000000 | 1 | 0.0800000 | <LOQ | NA | NA | 1 | ng/g | 0.037026547 | 0.123421824 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 40.4858300 | |
| F011 | Taylor_2019 | 2019 | Australia | E542 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxS | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 6.000000 | 6 | ng/g | 0.5991000 | NA | 0.2053000 | sd | biological | 6.000000 | 6 | 0.3870000 | NA | 0.079 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.2053000 | 0.079 | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E543 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxS | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | NA | Clean site | 1.000000 | 1 | ng/g | 0.1230000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 1.000000 | 1 | 0.0810000 | No sd, as N = 1 | NA | NA | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 40.4858300 | |
| F011 | Taylor_2019 | 2019 | Australia | E544 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOS | 8 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 6.000000 | 6 | ng/g | 5.0500000 | NA | 0.4637000 | sd | biological | 6.000000 | 6 | 5.5330000 | NA | 0.829 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.4637000 | 0.829 | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E545 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOS | 8 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | NA | Clean site | 6.000000 | 6 | ng/g | 0.1917000 | NA | 0.2129000 | sd | biological | 6.000000 | 6 | 0.1920000 | NA | 0.236 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.2129000 | 0.236 | 40.4858300 | |
| F011 | Taylor_2019 | 2019 | Australia | E546 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOS_Total | 8 | total | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 6.000000 | 6 | ng/g | 5.6833000 | NA | 0.4792000 | sd | biological | 6.000000 | 6 | 5.8670000 | NA | 0.885 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.4792000 | 0.885 | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E547 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOS_Total | 8 | total | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | NA | Clean site | 6.000000 | 6 | ng/g | 0.3433000 | NA | 0.3228000 | sd | biological | 6.000000 | 6 | 0.2180000 | NA | 0.249 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.3228000 | 0.249 | 40.4858300 | |
| F011 | Taylor_2019 | 2019 | Australia | E548 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | FOSA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 6.000000 | 6 | ng/g | 0.3112000 | NA | 0.1413000 | sd | biological | 6.000000 | 6 | 0.3220000 | NA | 0.099 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.1413000 | 0.099 | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E549 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFHpA | 7 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | NA | Contaminated site | 10.000000 | 1 | ng/g | 0.0802000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 10.000000 | 1 | 0.1280000 | NA | NA | biological | 1 | ng/g | 0.01867491 | 0.0622497 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | NA | NA | 7.7375426 | |
| F011 | Taylor_2019 | 2019 | Australia | E550 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOA | 8 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | NA | Contaminated site | 10.000000 | 1 | ng/g | 0.2229000 | NA | 0.0668000 | sd | biological | 10.000000 | 1 | 0.4690000 | NA | 0.104 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | 0.0668000 | 0.104 | 7.7375426 | |
| F011 | Taylor_2019 | 2019 | Australia | E551 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFNA | 9 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | NA | Contaminated site | 10.000000 | 1 | ng/g | 0.0910000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 10.000000 | 1 | 0.2330000 | NA | 0.037 | biological | 1 | ng/g | 0.036013573 | 0.120045244 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | NA | 0.037 | 7.7375426 | |
| F011 | Taylor_2019 | 2019 | Australia | E552 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFDA | 10 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | NA | Contaminated site | 10.000000 | 1 | ng/g | 0.0854000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 10.000000 | 1 | 0.1880000 | NA | 0.053 | biological | 1 | ng/g | 0.039417906 | 0.131393021 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | NA | 0.053 | 7.7375426 | |
| F011 | Taylor_2019 | 2019 | Australia | E553 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFHxS | 6 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | NA | Contaminated site | 10.000000 | 1 | ng/g | 2.3305000 | NA | 1.3905000 | sd | biological | 10.000000 | 1 | 6.3160000 | NA | 1.628 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | 1.3905000 | 1.628 | 7.7375426 | |
| F011 | Taylor_2019 | 2019 | Australia | E554 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOS | 8 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | NA | Contaminated site | 10.000000 | 1 | ng/g | 7.4167000 | NA | 2.8414000 | sd | biological | 10.000000 | 1 | 16.1670000 | NA | 3.869 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | 2.8414000 | 3.869 | 7.7375426 | |
| F011 | Taylor_2019 | 2019 | Australia | E555 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOS | 8 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 12.5376128 | 39.8800 | C144 | NA | Clean site | 10.000000 | 1 | ng/g | 0.0560000 | NA | 0.0133000 | sd | biological | 10.000000 | 1 | 0.1180000 | NA | 0.029 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | 0.0133000 | 0.029 | 12.5376128 | |
| F011 | Taylor_2019 | 2019 | Australia | E556 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOS_Total | 8 | total | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | NA | Contaminated site | 10.000000 | 1 | ng/g | 9.9500000 | NA | 3.4291000 | sd | biological | 10.000000 | 1 | 21.8333000 | NA | 5.076 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | 3.4291000 | 5.076 | 7.7375426 | |
| F011 | Taylor_2019 | 2019 | Australia | E557 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOS_Total | 8 | total | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 12.5376128 | 39.8800 | C144 | NA | Clean site | 10.000000 | 1 | ng/g | 0.0618000 | NA | 0.0182000 | sd | biological | 10.000000 | 1 | 0.1480000 | NA | 0.031 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | 0.0182000 | 0.031 | 12.5376128 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E557 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.74 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | Shared control | NA | 7.666667 | 1 | ng/g | 1.5000000 | NA | 0.0400000 | sd | technical | 7.666667 | 1 | 1.7500000 | NA | 0.05 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | ML - why “Se_technical_biological” is coded as “sd”? “If_technical_how_many” needs a number. Shared control between differend cooking methods | NA | NA | 0.9000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E558 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.74 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | Shared control | NA | 7.666667 | 1 | ng/g | 1.8600000 | NA | 0.1900000 | sd | technical | 7.666667 | 1 | 2.9900000 | NA | 0.22 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.9000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E559 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.74 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | Shared control | NA | 7.666667 | 1 | ng/g | 3.0600000 | NA | 0.1000000 | sd | technical | 7.666667 | 1 | 6.6200000 | NA | 0.14 | technical | 1 | ng/g | 0.49 | 1.48 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.9000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E560 | Bogue | Boops boops | vertebrate | marine fish | 18.35 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | Shared control | NA | 4.000000 | 1 | ng/g | 0.2400000 | NA | 0.0300000 | sd | technical | 4.000000 | 1 | 0.4400000 | NA | 0.02 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3600000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E561 | Bogue | Boops boops | vertebrate | marine fish | 18.35 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | Shared control | NA | 4.000000 | 1 | ng/g | 0.5600000 | NA | 0.0800000 | sd | technical | 4.000000 | 1 | 1.1200000 | NA | 0.03 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3600000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E562 | Bogue | Boops boops | vertebrate | marine fish | 18.35 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | Shared control | NA | 4.000000 | 1 | ng/g | 0.8200000 | NA | 0.0400000 | sd | technical | 4.000000 | 1 | 1.2700000 | NA | 0.06 | technical | 1 | ng/g | 0.49 | 1.48 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3600000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E563 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.00 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | Shared control | NA | 6.666667 | 1 | ng/g | 0.4200000 | NA | 0.0500000 | sd | technical | 6.666667 | 1 | 0.7000000 | LOD | NA | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4300000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E564 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.00 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | Shared control | NA | 6.666667 | 1 | ng/g | 0.6200000 | NA | 0.0800000 | sd | technical | 6.666667 | 1 | 0.1000000 | <LOD | NA | NA | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4300000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E565 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.00 | PFBS | 4 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | Shared control | NA | 6.666667 | 1 | ng/g | 0.4500000 | NA | 0.0700000 | sd | technical | 6.666667 | 1 | 0.8300000 | NA | 0.03 | technical | 1 | ng/g | 0.57 | 1.7 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4300000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E566 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.00 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | Shared control | NA | 6.666667 | 1 | ng/g | 0.8400000 | NA | 0.1000000 | sd | technical | 6.666667 | 1 | 1.2400000 | NA | 0.06 | technical | 1 | ng/g | 0.49 | 1.48 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4300000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E567 | Picarel | Spicara smaris | vertebrate | marine fish | 44.04 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4700000 | 638.2979 | C148 | Shared control | NA | 6.666667 | 1 | ng/g | 0.7000000 | NA | 0.0900000 | sd | technical | 6.666667 | 1 | 1.3500000 | NA | 0.08 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E568 | Picarel | Spicara smaris | vertebrate | marine fish | 44.04 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4700000 | 638.2979 | C148 | Shared control | NA | 6.666667 | 1 | ng/g | 20.3700000 | NA | 2.4700000 | sd | technical | 6.666667 | 1 | 44.6900000 | NA | 3.93 | technical | 1 | ng/g | 0.49 | 1.48 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E569 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.11 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | Shared control | NA | 13.000000 | 1 | ng/g | 0.3500000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 13.000000 | 1 | 0.7400000 | NA | 0.09 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5200000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E570 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.11 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | Shared control | NA | 13.000000 | 1 | ng/g | 1.0800000 | NA | 0.0300000 | sd | technical | 13.000000 | 1 | 1.9800000 | NA | 0.04 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5200000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E571 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.11 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | Shared control | NA | 13.000000 | 1 | ng/g | 1.1600000 | NA | 0.0500000 | sd | technical | 13.000000 | 1 | 3.0100000 | NA | 0.13 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5200000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E572 | Sardine | Sardina pilchardus | vertebrate | marine fish | 57.26 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.8800000 | 340.9091 | C150 | Shared control | NA | 4.666667 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 4.666667 | 1 | 0.9300000 | NA | 0.03 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.8800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E573 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.32 | PFNA | 9 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | Shared control | NA | 5.000000 | 1 | ng/g | 0.6000000 | NA | 0.0300000 | sd | technical | 5.000000 | 1 | 0.5700000 | NA | 0.11 | technical | 1 | ng/g | 0.42 | 1.25 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E574 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.32 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | Shared control | NA | 5.000000 | 1 | ng/g | 0.6500000 | NA | 0.0600000 | sd | technical | 5.000000 | 1 | 0.5600000 | NA | 0.07 | technical | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E575 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.32 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | Shared control | NA | 5.000000 | 1 | ng/g | 1.0500000 | NA | 0.1300000 | sd | technical | 5.000000 | 1 | 0.7300000 | NA | 0.2 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E576 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.32 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | Shared control | NA | 5.000000 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | technical | 5.000000 | 1 | 1.3800000 | NA | 0.07 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E577 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.32 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | Shared control | NA | 5.000000 | 1 | ng/g | 5.6600000 | NA | 0.1500000 | sd | technical | 5.000000 | 1 | 0.1000000 | <LOD | NA | NA | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E578 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFPeA | 5 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 4.9400000 | NA | 0.2600000 | sd | technical | 10.000000 | 1 | 14.8800000 | NA | 1.61 | technical | 1 | ng/g | 0.39 | 1.17 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E579 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFOA | 8 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 0.3000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | technical | 10.000000 | 1 | 0.9900000 | NA | 0.21 | technical | 1 | ng/g | 0.6 | 1.82 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E580 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFNA | 9 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 1.2700000 | NA | 0.0700000 | sd | technical | 10.000000 | 1 | 1.5200000 | NA | 0.11 | technical | 1 | ng/g | 0.42 | 1.25 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E581 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 1.7300000 | NA | 0.0800000 | sd | technical | 10.000000 | 1 | 1.8100000 | NA | 0.19 | technical | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E582 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 2.7600000 | NA | 0.2100000 | sd | technical | 10.000000 | 1 | 6.8200000 | NA | 0.22 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E583 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 1.3600000 | NA | 0.0900000 | sd | technical | 10.000000 | 1 | 2.3100000 | NA | 0.09 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E584 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFBS | 4 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 1.3700000 | NA | 0.1600000 | sd | technical | 10.000000 | 1 | 0.2850000 | <LOD | NA | NA | 1 | ng/g | 0.57 | 1.7 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E585 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 5.1500000 | NA | 0.3900000 | sd | technical | 10.000000 | 1 | 8.0200000 | NA | 0.42 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E586 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.87 | PFPeA | 5 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | Shared control | NA | 6.000000 | 1 | ng/g | 0.1950000 | <LOD | NA | sd | technical | 6.000000 | 1 | 5.0600000 | NA | 0.19 | technical | 1 | ng/g | 0.39 | 1.17 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3900000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E587 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.87 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | Shared control | NA | 6.000000 | 1 | ng/g | 0.3450000 | <LOD | NA | sd | technical | 6.000000 | 1 | 0.5100000 | NA | 0.04 | technical | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3900000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E588 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.87 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | Shared control | NA | 6.000000 | 1 | ng/g | 0.3500000 | <LOD | NA | sd | technical | 6.000000 | 1 | 1.0400000 | NA | 0.02 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3900000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E589 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.87 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | Shared control | NA | 6.000000 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 6.000000 | 1 | 1.6500000 | NA | 0.07 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3900000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E590 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.87 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | Shared control | NA | 6.000000 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 6.000000 | 1 | 1.5600000 | NA | 0.17 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3900000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E591 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.16 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | Shared control | NA | 13.000000 | 1 | ng/g | 0.3450000 | <LOD | NA | sd | technical | 13.000000 | 1 | 0.8300000 | NA | 0.01 | technical | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E592 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.16 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | Shared control | NA | 13.000000 | 1 | ng/g | 1.5000000 | NA | 0.0400000 | sd | technical | 13.000000 | 1 | 2.7300000 | NA | 0.13 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E593 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.16 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | Shared control | NA | 13.000000 | 1 | ng/g | 1.8600000 | NA | 0.1900000 | sd | technical | 13.000000 | 1 | 3.5200000 | NA | 0.1 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E594 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.16 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | Shared control | NA | 13.000000 | 1 | ng/g | 3.0600000 | NA | 0.1000000 | sd | technical | 13.000000 | 1 | 6.2900000 | NA | 0.34 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E595 | Bogue | Boops boops | vertebrate | marine fish | 7.44 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | Shared control | NA | 4.000000 | 1 | ng/g | 0.2400000 | NA | 0.0300000 | sd | technical | 4.000000 | 1 | 0.4300000 | NA | 0.03 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E596 | Bogue | Boops boops | vertebrate | marine fish | 7.44 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | Shared control | NA | 4.000000 | 1 | ng/g | 0.5600000 | NA | 0.0800000 | sd | technical | 4.000000 | 1 | 0.6300000 | NA | 0.02 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E597 | Bogue | Boops boops | vertebrate | marine fish | 7.44 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | Shared control | NA | 4.000000 | 1 | ng/g | 0.8200000 | NA | 0.0400000 | sd | technical | 4.000000 | 1 | 0.8700000 | NA | 0.07 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E598 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.91 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | Shared control | NA | 6.666667 | 1 | ng/g | 0.3450000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 6.666667 | 1 | 0.8200000 | NA | 0.03 | technical | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E599 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.91 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | Shared control | NA | 6.666667 | 1 | ng/g | 0.4200000 | NA | 0.0500000 | sd | technical | 6.666667 | 1 | 1.1100000 | NA | 0.15 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E600 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.91 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | Shared control | NA | 6.666667 | 1 | ng/g | 0.6200000 | NA | 0.0800000 | sd | technical | 6.666667 | 1 | 1.8900000 | NA | 0.05 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E601 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.91 | PFBS | 4 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | Shared control | NA | 6.666667 | 1 | ng/g | 0.4500000 | NA | 0.0700000 | sd | technical | 6.666667 | 1 | 0.2850000 | <LOD | NA | NA | 1 | ng/g | 0.57 | 1.7 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E602 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.91 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | Shared control | NA | 6.666667 | 1 | ng/g | 0.8400000 | NA | 0.1000000 | sd | technical | 6.666667 | 1 | 2.4000000 | NA | 0.13 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E603 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.95 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | Shared control | NA | 4.666667 | 1 | ng/g | 0.3450000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 4.666667 | 1 | 0.8700000 | NA | 0.03 | technical | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E604 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.95 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | Shared control | NA | 4.666667 | 1 | ng/g | 0.3500000 | <LOD | NA | sd | technical | 4.666667 | 1 | 1.7000000 | NA | 0.13 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E605 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.95 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | Shared control | NA | 4.666667 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 4.666667 | 1 | 3.1900000 | NA | 0.09 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E606 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.66 | PFNA | 9 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | Shared control | NA | 5.000000 | 1 | ng/g | 0.6000000 | NA | 0.0300000 | sd | technical | 5.000000 | 1 | 0.5000000 | NA | 0.05 | technical | 1 | ng/g | 0.42 | 1.25 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E607 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.66 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | Shared control | NA | 5.000000 | 1 | ng/g | 0.6500000 | NA | 0.0600000 | sd | technical | 5.000000 | 1 | 0.3450000 | <LOD | NA | NA | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E608 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.66 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | Shared control | NA | 5.000000 | 1 | ng/g | 1.0500000 | NA | 0.1300000 | sd | technical | 5.000000 | 1 | 0.8200000 | NA | 0.02 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E609 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.66 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | Shared control | NA | 5.000000 | 1 | ng/g | 5.6600000 | NA | 0.1500000 | sd | technical | 5.000000 | 1 | 10.2300000 | NA | 0.53 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E610 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.29 | PFOA | 8 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | Shared control | NA | 6.000000 | 1 | ng/g | 0.3000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 6.000000 | 1 | 0.4000000 | NA | 0.01 | technical | 1 | ng/g | 0.6 | 1.82 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E611 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.29 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | Shared control | NA | 6.000000 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 6.000000 | 1 | 1.0900000 | NA | 0.02 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E612 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.29 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | Shared control | NA | 6.000000 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 6.000000 | 1 | 1.1900000 | NA | 0.17 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 |
The phylogenetic tree was generated in the tree_cooked_fish_MA.Rmd document
tree <- read.tree(here("data", "phylogenetic_tree.tre")) # Import phylogenetic tree (see tree_cooked_fish_MA.Rmd for more details)
tree <- compute.brlen(tree) # Generate branch lengths
cor_tree <- vcv(tree, corr = T) # Generate phylogenetic variance-covariance matrix
dat$Phylogeny <- as.factor(str_replace(dat$Species_Scientific, " ", "_")) # Add the `phylogeny` column to the data frame
colnames(cor_tree) %in% dat$Phylogeny # Check correspondence between tip names and data frame## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
# checking all species are in the data
# match(unique(dat$Phylogeny),colnames(cor_tree))
match(dat$Phylogeny, colnames(cor_tree))## [1] 12 14 28 28 28 28 28 28 13 32 32 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [26] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 2 2 2 2 2 2 2 2 2
## [51] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 7
## [76] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [101] 7 7 7 7 7 7 7 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
## [126] 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 3 26 26 18 18 18 18 34 34 34
## [151] 34 34 34 22 22 31 31 30 30 30 30 30 23 23 23 23 1 1 1 1 1 1 1 1 1
## [176] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 15
## [201] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 38 38 38
## [226] 38 38 38 38 38 38 38 38 38 38 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
## [251] 16 16 16 16 16 16 16 16 16 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27
## [276] 27 27 27 27 27 27 27 27 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [301] 8 8 8 8 8 8 8 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
## [326] 19 19 19 19 19 19 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [351] 9 9 9 9 9 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [376] 14 14 14 14 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
## [401] 17 17 17 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [426] 11 11 24 24 24 24 24 24 24 24 24 24 24 24 24 37 37 37 37 37 37 37 37 37 37
## [451] 37 37 37 37 37 35 35 35 35 35 35 35 35 35 5 5 5 20 20 20 29 29 29 29 21
## [476] 21 10 10 10 4 27 27 27 27 27 36 36 36 36 36 36 36 36 33 33 33 33 33 5 5
## [501] 5 5 20 20 20 29 29 29 29 29 4 4 4 27 27 27 27 33 33 33
# plotting tree
plot(tree)The average coefficient of variation in PFAS concentration was calculated for each study and treatment, according to Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644. Then, these values were averaged across studies and used to calculate the lnRR corrected for small sample sizes (for formula, see the lnRR_func above)
dat$Study_ID <- as.factor(dat$Study_ID)
dat$SDe <- as.numeric(dat$SDe)
# Calculate the squared coefficient of variation for control and experimental groups
aCV2 <- dat %>%
group_by(Study_ID) %>% # Group by study
summarise(CV2c = mean((SDc/Mc)^2, na.rm = T),
CV2e = mean((SDe/Me)^2, na.rm = T)) %>%
ungroup() %>% # ungroup
summarise(aCV2c = mean(CV2c, na.rm = T), # Mean CV^2 for exp and control groups across studies
aCV2e = mean(CV2e, na.rm = T))
lnRR <- # Calculate effect sizes
lnRR_func(Mc = dat$Mc,
Nc = dat$Nc,
Me = dat$Me,
Ne = dat$Ne,
aCV2c = aCV2[[1]],
aCV2e = aCV2[[2]])
var_lnRR <- ifelse(dat$Design == "Dependent", # Calculate sampling variance
var_lnRR_dep(Mc = dat$Mc,
Nc = dat$Nc,
Me = dat$Me,
Ne = dat$Ne,
aCV2c = aCV2[[1]],
aCV2e = aCV2[[2]],
rho = 0.5),
var_lnRR_ind(Mc = dat$Mc,
Nc = dat$Nc,
Me = dat$Me,
Ne = dat$Ne,
aCV2c = aCV2[[1]],
aCV2e = aCV2[[2]]))
dat <- dat %>%
mutate(N_tilde = (Nc*Ne)/(Nc + Ne)) # getting effective sample size
dat <- cbind(dat, lnRR, var_lnRR) # Merge effect sizes with the data frame
VCV_lnRR <- make_VCV_matrix(dat, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # Because some effect sizes share the same control, we generated a variance-covariance matrix to account for correlated errors (i.e. effectively dividing the weight of the correlated estimates by half)# mean
ggplot(dat, aes(x = lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.2) +
theme_classic()# variance
ggplot(dat, aes(x = var_lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) +
theme_classic()# log variance
ggplot(dat, aes(x = var_lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) +
scale_x_log10() + theme_classic()dat %>%
summarise( # Calculate the number of effect sizes, studies and species for the main categorical variables
`Studies` = n_distinct(Study_ID),
`Species` = n_distinct(Species_common),
`PFAS type` = n_distinct(PFAS_type),
`Cohorts` = n_distinct(Cohort_ID),
`Effect sizes` = n_distinct(Effect_ID),
`Effect sizes (Oil-based)` = n_distinct(Effect_ID[Cooking_Category=="oil-based"]),
`Studies (Oil-based)` = n_distinct(Study_ID[Cooking_Category=="oil-based"]),
`Species (Oil-based)` = n_distinct(Species_common[Cooking_Category=="oil-based"]),
`Effect sizes (Water-based)` = n_distinct(Effect_ID[Cooking_Category=="water-based"]),
`Studies (Water-based)` = n_distinct(Study_ID[Cooking_Category=="water-based"]),
`Species (Water-based)` = n_distinct(Species_common[Cooking_Category=="water-based"]),
`Effect sizes (No liquid)` = n_distinct(Effect_ID[Cooking_Category=="No liquid"]),
`Studies (No liquid)` = n_distinct(Study_ID[Cooking_Category=="No liquid"]),
`Species (No liquid)` = n_distinct(Species_common[Cooking_Category=="No liquid"]),) -> table_sample_sizes
table_sample_sizes<-t(table_sample_sizes)
colnames(table_sample_sizes)<-"n (sample size)"
kable(table_sample_sizes) %>% kable_styling("striped", position="left")| n (sample size) | |
|---|---|
| Studies | 10 |
| Species | 39 |
| PFAS type | 19 |
| Cohorts | 153 |
| Effect sizes | 519 |
| Effect sizes (Oil-based) | 307 |
| Studies (Oil-based) | 7 |
| Species (Oil-based) | 28 |
| Effect sizes (Water-based) | 144 |
| Studies (Water-based) | 8 |
| Species (Water-based) | 23 |
| Effect sizes (No liquid) | 69 |
| Studies (No liquid) | 2 |
| Species (No liquid) | 14 |
kable(summary(dat), "html") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")| Study_ID | Author_year | Publication_year | Country_firstAuthor | Effect_ID | Species_common | Species_Scientific | Invertebrate_vertebrate | Fish_mollusc | Moisture_loss_in_percent | PFAS_type | PFAS_carbon_chain | linear_total | Choice_of_9 | Cooking_method | Cooking_Category | Comments_cooking | Temperature_in_Celsius | Length_cooking_time_in_s | Water | Oil | Oil_type | Volume_liquid_ml | Volume_liquid_ml_0 | Ratio_liquid_fish | Weigh_g_sample | Cohort_ID | Cohort_comment | Cohort_comment_2 | Nc | Pooled_Nc | Unit_PFAS_conc | Mc | Mc_comment | Sc | sd | Sc_technical_biological | Ne | Pooled_Ne | Me | Me_comment | Se | Se_technical_biological | If_technical_how_many | Unit_LOD_LOQ | LOD | LOQ | Design | DataSource | Raw_data_provided | General_comments | checked | SDc | SDe | Ratio_liquid_fish_0 | Phylogeny | N_tilde | lnRR | var_lnRR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| F010 :192 | Length:520 | Min. :2008 | Length:520 | Length:520 | Length:520 | Length:520 | Length:520 | Length:520 | Min. : 6.77 | Length:520 | Min. : 3.000 | Length:520 | Length:520 | Length:520 | Length:520 | Length:520 | Min. : 75.0 | Min. : 120.0 | Length:520 | Length:520 | Length:520 | Min. : 0.341 | Min. : 0.0 | Min. : 0.00266 | Min. : 10.0 | Length:520 | Length:520 | Length:520 | Min. : 1.000 | Min. :1.000 | Length:520 | Min. : 0.002 | Length:520 | Min. : 0.0010 | Length:520 | Length:520 | Min. : 1.000 | Min. :1.000 | Min. : 0.00200 | Length:520 | Length:520 | Length:520 | Length:520 | Length:520 | Length:520 | Length:520 | Length:520 | Length:520 | Length:520 | Length:520 | Length:520 | Min. : 0.0010 | Min. : 0.0010 | Min. : 0.00000 | Cyprinus_carpio : 33 | Min. : 0.500 | Min. :-6.0349 | Min. :0.01722 | |
| F003 :129 | Class :character | 1st Qu.:2014 | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.:14.45 | Class :character | 1st Qu.: 8.000 | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.:100.0 | 1st Qu.: 600.0 | Class :character | Class :character | Class :character | 1st Qu.: 11.000 | 1st Qu.: 10.0 | 1st Qu.: 0.10004 | 1st Qu.: 10.0 | Class :character | Class :character | Class :character | 1st Qu.: 5.000 | 1st Qu.:1.000 | Class :character | 1st Qu.: 0.159 | Class :character | 1st Qu.: 0.0010 | Class :character | Class :character | 1st Qu.: 5.000 | 1st Qu.:1.000 | 1st Qu.: 0.09575 | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.: 0.0342 | 1st Qu.: 0.0585 | 1st Qu.: 0.05116 | Mullus_barbatus : 33 | 1st Qu.: 2.500 | 1st Qu.:-0.8505 | 1st Qu.:0.08612 | |
| F013 : 56 | Mode :character | Median :2019 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median :18.64 | Mode :character | Median : 8.000 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median :160.0 | Median : 600.0 | Mode :character | Mode :character | Mode :character | Median : 300.000 | Median : 250.0 | Median : 2.50000 | Median : 70.0 | Mode :character | Mode :character | Mode :character | Median :10.000 | Median :1.000 | Mode :character | Median : 0.298 | Mode :character | Median : 0.0110 | Mode :character | Mode :character | Median :10.000 | Median :1.000 | Median : 0.22850 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median : 0.1580 | Median : 0.1545 | Median : 0.57000 | Salvelinus_namaycush : 33 | Median : 5.000 | Median :-0.1538 | Median :0.11096 | |
| F011 : 37 | NA | Mean :2017 | NA | NA | NA | NA | NA | NA | Mean :21.03 | NA | Mean : 8.979 | NA | NA | NA | NA | NA | Mean :160.2 | Mean : 726.6 | NA | NA | NA | Mean : 271.809 | Mean : 232.3 | Mean :13.58236 | Mean : 147.0 | NA | NA | NA | Mean : 8.458 | Mean :2.348 | NA | Mean : 3.793 | NA | Mean : 2.3538 | NA | NA | Mean : 8.458 | Mean :2.348 | Mean : 3.60951 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Mean : 5.7366 | Mean : 5.8324 | Mean :11.71173 | Sander_vitreus : 33 | Mean : 4.229 | Mean :-0.3537 | Mean :0.13945 | |
| F006 : 32 | NA | 3rd Qu.:2019 | NA | NA | NA | NA | NA | NA | 3rd Qu.:21.31 | NA | 3rd Qu.:11.000 | NA | NA | NA | NA | NA | 3rd Qu.:175.0 | 3rd Qu.: 900.0 | NA | NA | NA | 3rd Qu.: 300.000 | 3rd Qu.: 300.0 | 3rd Qu.:30.00000 | 3rd Qu.: 177.8 | NA | NA | NA | 3rd Qu.:10.000 | 3rd Qu.:5.000 | NA | 3rd Qu.: 1.130 | NA | 3rd Qu.: 0.1421 | NA | NA | 3rd Qu.:10.000 | 3rd Qu.:5.000 | 3rd Qu.: 1.08550 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3rd Qu.: 0.6252 | 3rd Qu.: 0.7578 | 3rd Qu.:30.00000 | Ctenopharyngodon_idell : 32 | 3rd Qu.: 5.000 | 3rd Qu.: 0.1880 | 3rd Qu.:0.17223 | |
| F005 : 26 | NA | Max. :2020 | NA | NA | NA | NA | NA | NA | Max. :79.11 | NA | Max. :14.000 | NA | NA | NA | NA | NA | Max. :300.0 | Max. :1500.0 | NA | NA | NA | Max. :2500.000 | Max. :2500.0 | Max. :45.33092 | Max. :1000.0 | NA | NA | NA | Max. :50.000 | Max. :6.000 | NA | Max. :86.689 | NA | Max. :141.7900 | NA | NA | Max. :50.000 | Max. :6.000 | Max. :134.43792 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Max. :141.7900 | Max. :164.5000 | Max. :45.33092 | Oncorhynchus_tshawytscha: 30 | Max. :25.000 | Max. : 3.4600 | Max. :0.86115 | |
| (Other): 48 | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :288 | NA | NA | NA | NA | NA | NA | NA | NA’s :6 | NA’s :56 | NA | NA | NA | NA’s :114 | NA’s :45 | NA’s :88 | NA’s :106 | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :53 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :330 | NA’s :328 | NA’s :19 | (Other) :326 | NA | NA | NA |
Cohort_ID and Phylogeny explained virtually no variance in the model. Hence, they was removed from the model. All the other random effects explained significant variance and were kept in subsequent models
MA_all_rand_effects <- rma.mv(lnRR, VCV_lnRR, # Add `VCV_lnRR` to account for correlated errors errors between cohorts (shared_controls)
random = list(~1|Study_ID, # Identity of the study
~1|Phylogeny, # Phylogenetic correlation
~1|Cohort_ID, # Identity of the cohort (shared controls)
~1|Species_common, # Non-phylogenetic correlation between species
~1|PFAS_type, # Type of PFAS
~1|Effect_ID), # Effect size identity
R= list(Phylogeny = cor_tree), # Assign the 'Phylogeny' argument to the phylogenetic variance-covariance matrix
test = "t",
data = dat,
sparse = TRUE)
summary(MA_all_rand_effects) # Cohort ID does not explain any variance ##
## Multivariate Meta-Analysis Model (k = 520; method: REML)
##
## logLik Deviance AIC BIC AICc
## -628.9903 1257.9807 1271.9807 1301.7440 1272.1998
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5862 0.7656 10 no Study_ID no
## sigma^2.2 0.0000 0.0001 38 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 153 no Cohort_ID no
## sigma^2.4 0.1907 0.4367 39 no Species_common no
## sigma^2.5 0.0928 0.3046 19 no PFAS_type no
## sigma^2.6 0.4693 0.6851 519 no Effect_ID no
##
## Test for Heterogeneity:
## Q(df = 519) = 7298.1770, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.3273 0.2822 -1.1599 519 0.2466 -0.8816 0.2271
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MA_model <- rma.mv(lnRR, VCV_lnRR,
random = list(~1|Study_ID,
~1|Species_common, # Removed Cohort_ID and phylogeny
~1|PFAS_type,
~1|Effect_ID),
test = "t",
data = dat,
sparse = TRUE)
summary(MA_model)##
## Multivariate Meta-Analysis Model (k = 520; method: REML)
##
## logLik Deviance AIC BIC AICc
## -628.9903 1257.9807 1267.9807 1289.2402 1268.0976
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5862 0.7656 10 no Study_ID
## sigma^2.2 0.1907 0.4367 39 no Species_common
## sigma^2.3 0.0928 0.3046 19 no PFAS_type
## sigma^2.4 0.4693 0.6851 519 no Effect_ID
##
## Test for Heterogeneity:
## Q(df = 519) = 7298.1770, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.3273 0.2822 -1.1599 519 0.2466 -0.8816 0.2271
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
round(i2_ml(MA_model)*100,2) # Percentage of heterogeneity explained by each random effect## I2_Total I2_Study_ID I2_Species_common I2_PFAS_type
## 9268.92 4057.59 1320.05 642.40
## I2_Effect_ID
## 3248.88
orchard_plot(MA_model, mod = "1", xlab = "lnRR", alpha=0.4, data = dat, group = "Study_ID", trunk.size=9, branch.size = 2) +
scale_colour_manual(values = "darkorange")+ # change colours
scale_fill_manual(values="darkorange")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) save(MA_model, MA_all_rand_effects, file = here("Rdata", "int_MA_models.RData")) # save the models run_model<-function(data,formula){
data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix
VCV<-make_VCV_matrix(data
, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
rma.mv(lnRR, VCV, # run the model, as described earlier
mods=formula,
random = list(~1|Study_ID,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
test = "t",
data = data,
sparse=TRUE) # Make the model run faster
}plot_continuous<-function(data, model, moderator, xlab){
pred<-predict.rma(model)
data %>% mutate(fit=pred$pred,
ci.lb=pred$ci.lb,
ci.ub=pred$ci.ub,
pr.lb=pred$cr.lb,
pr.ub=pred$cr.ub) %>% # Add confidence intervals, mean predictions and prediction intervals
ggplot(aes(x = moderator, y = lnRR)) +
geom_ribbon(aes(ymin = pr.lb, ymax = pr.ub, color = NULL), alpha = .075) + # Shaded area for prediction intervals
geom_ribbon(aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = .2) + # Shaded area for confidence intervals
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) + # Points scaled by precision
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
geom_line(aes(y = fit), size = 1.5)+ # Regression line
labs(x = xlab, y = "lnRR", size = "Precison (1/SE)") +
theme_bw() +
scale_size_continuous(range=c(1,9))+ # Point scaling
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
}All continuous variables were z-transformed
# Length_cooking_time_in_s
time_model <- run_model(dat, ~scale(Length_cooking_time_in_s)) # z-transformed
summary(time_model)##
## Multivariate Meta-Analysis Model (k = 464; method: REML)
##
## logLik Deviance AIC BIC AICc
## -522.0518 1044.1036 1056.1036 1080.9170 1056.2882
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5303 0.7282 9 no Study_ID
## sigma^2.2 0.1724 0.4152 30 no Species_common
## sigma^2.3 0.0933 0.3055 18 no PFAS_type
## sigma^2.4 0.4021 0.6341 464 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 462) = 6461.9273, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 462) = 27.7927, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5462 0.2858 -1.9109 462 0.0566
## scale(Length_cooking_time_in_s) -0.2585 0.0490 -5.2719 462 <.0001
## ci.lb ci.ub
## intrcpt -1.1078 0.0155 .
## scale(Length_cooking_time_in_s) -0.3549 -0.1622 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(time_model) # Estimate R squared## R2_marginal R2_conditional
## 0.0528416 0.6821055
# Plot
dat.time <- filter(dat, Length_cooking_time_in_s != "NA") # Need to remove the NAs from the data
plot_continuous(dat.time, time_model, dat.time$Length_cooking_time_in_s, "Cooking time (s)")NA# Ratio_liquid_fish
dat <- dat %>%
mutate(Ratio_liquid_fish_0 = ifelse(Cooking_Category == "No liquid", 0, Ratio_liquid_fish)) # Add a 0 when the cooking category is 'No liquid', otherwise keep the same value of Ratio_liquid_fish
volume_model <- run_model(dat, ~scale(log(Ratio_liquid_fish))) # logged and z-transformed
summary(volume_model)##
## Multivariate Meta-Analysis Model (k = 432; method: REML)
##
## logLik Deviance AIC BIC AICc
## -534.7059 1069.4119 1081.4119 1105.7946 1081.6105
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5497 0.7414 8 no Study_ID
## sigma^2.2 0.1142 0.3380 35 no Species_common
## sigma^2.3 0.1066 0.3266 19 no PFAS_type
## sigma^2.4 0.5117 0.7153 431 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 430) = 5878.7099, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 430) = 4.3319, p-val = 0.0380
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.4475 0.2949 -1.5177 430 0.1298 -1.0272
## scale(log(Ratio_liquid_fish)) -0.2542 0.1221 -2.0813 430 0.0380 -0.4942
## ci.ub
## intrcpt 0.1321
## scale(log(Ratio_liquid_fish)) -0.0141 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(volume_model)## R2_marginal R2_conditional
## 0.04796838 0.62008694
# Plot
dat.volume <- filter(dat, Ratio_liquid_fish != "NA")
plot_continuous(dat.volume, volume_model, log(dat.volume$Ratio_liquid_fish), "ln (Liquid volume to tissue sample ratio)")0volume0_model <- run_model(dat, ~scale(log(Ratio_liquid_fish_0 + 1))) # logged and z-transformed after adding 1
summary(volume0_model)##
## Multivariate Meta-Analysis Model (k = 501; method: REML)
##
## logLik Deviance AIC BIC AICc
## -600.2637 1200.5274 1212.5274 1237.8030 1212.6981
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.6122 0.7824 8 no Study_ID
## sigma^2.2 0.1791 0.4232 35 no Species_common
## sigma^2.3 0.1164 0.3412 19 no PFAS_type
## sigma^2.4 0.4582 0.6769 500 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 499) = 6266.9390, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 499) = 4.3910, p-val = 0.0366
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.4400 0.3134 -1.4039 499 0.1610
## scale(log(Ratio_liquid_fish_0 + 1)) -0.1132 0.0540 -2.0955 499 0.0366
## ci.lb ci.ub
## intrcpt -1.0558 0.1758
## scale(log(Ratio_liquid_fish_0 + 1)) -0.2194 -0.0071 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(volume0_model)## R2_marginal R2_conditional
## 0.009296736 0.667670150
# Plot
dat.volume0 <- filter(dat, Ratio_liquid_fish_0 != "NA")
plot_continuous(dat.volume0, volume0_model, log(dat.volume0$Ratio_liquid_fish_0 +
1), "ln (Liquid volume to tissue sample ratio + 1)")# Temperature_in_Celsius
temp_model <- run_model(dat, ~scale(Temperature_in_Celsius)) # z-transformed
summary(temp_model)##
## Multivariate Meta-Analysis Model (k = 514; method: REML)
##
## logLik Deviance AIC BIC AICc
## -620.3869 1240.7738 1252.7738 1278.2038 1252.9402
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5805 0.7619 10 no Study_ID
## sigma^2.2 0.1880 0.4336 39 no Species_common
## sigma^2.3 0.0897 0.2996 19 no PFAS_type
## sigma^2.4 0.4703 0.6858 513 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 512) = 7174.7080, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 512) = 0.0259, p-val = 0.8721
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.3123 0.2825 -1.1056 512 0.2694 -0.8673
## scale(Temperature_in_Celsius) 0.0115 0.0716 0.1611 512 0.8721 -0.1292
## ci.ub
## intrcpt 0.2427
## scale(Temperature_in_Celsius) 0.1522
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(temp_model)## R2_marginal R2_conditional
## 0.0001001568 0.6460357560
# Plot
dat.temp <- filter(dat, Temperature_in_Celsius != "NA")
plot_continuous(dat.temp, temp_model, dat.temp$Temperature_in_Celsius, "Cooking temperature")# PFAS_carbon_chain
PFAS_model <- run_model(dat, ~PFAS_carbon_chain)
summary(PFAS_model)##
## Multivariate Meta-Analysis Model (k = 520; method: REML)
##
## logLik Deviance AIC BIC AICc
## -627.5304 1255.0609 1267.0609 1292.5607 1267.2253
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5853 0.7650 10 no Study_ID
## sigma^2.2 0.1926 0.4388 39 no Species_common
## sigma^2.3 0.0969 0.3112 19 no PFAS_type
## sigma^2.4 0.4695 0.6852 519 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 518) = 7298.0312, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 518) = 0.1673, p-val = 0.6827
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.4386 0.3916 -1.1200 518 0.2632 -1.2079 0.3307
## PFAS_carbon_chain 0.0123 0.0301 0.4090 518 0.6827 -0.0468 0.0714
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(PFAS_model)## R2_marginal R2_conditional
## 0.0006026939 0.6509239599
plot_continuous(dat, PFAS_model, dat$PFAS_carbon_chain, "PFAS carbon chain length")# Cooking_Category
category_model<-run_model(dat, ~Cooking_Category-1)
summary(category_model)##
## Multivariate Meta-Analysis Model (k = 520; method: REML)
##
## logLik Deviance AIC BIC AICc
## -625.8150 1251.6301 1265.6301 1295.3664 1265.8501
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5902 0.7682 10 no Study_ID
## sigma^2.2 0.1932 0.4396 39 no Species_common
## sigma^2.3 0.0937 0.3062 19 no PFAS_type
## sigma^2.4 0.4688 0.6847 519 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 517) = 7296.1935, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 517) = 1.3280, p-val = 0.2644
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Cooking_CategoryNo liquid -0.1996 0.3049 -0.6545 517 0.5131 -0.7987
## Cooking_Categoryoil-based -0.3902 0.2899 -1.3461 517 0.1789 -0.9596
## Cooking_Categorywater-based -0.2968 0.2879 -1.0309 517 0.3031 -0.8625
## ci.ub
## Cooking_CategoryNo liquid 0.3995
## Cooking_Categoryoil-based 0.1793
## Cooking_Categorywater-based 0.2688
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(category_model)## R2_marginal R2_conditional
## 0.003425853 0.652886711
# plot
orchard_plot(category_model, mod = "Cooking_Category", xlab = "lnRR", alpha=0.4, data = dat, group = "Study_ID", trunk.size=9, branch.size = 2) +
scale_colour_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3"))+ # change colours
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))This analysis is a posteriori and will only be presented in supplement.
# Moisture_loss_in_percent
moisture_model <- run_model(dat, ~scale(Moisture_loss_in_percent))
summary(moisture_model)##
## Multivariate Meta-Analysis Model (k = 232; method: REML)
##
## logLik Deviance AIC BIC AICc
## -213.6854 427.3708 439.3708 459.9993 439.7475
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0539 0.2322 6 no Study_ID
## sigma^2.2 0.2387 0.4886 18 no Species_common
## sigma^2.3 0.0092 0.0959 18 no PFAS_type
## sigma^2.4 0.2036 0.4512 232 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 230) = 1136.4740, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 230) = 0.0030, p-val = 0.9563
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.3508 0.1802 1.9465 230 0.0528 -0.0043
## scale(Moisture_loss_in_percent) 0.0043 0.0779 0.0548 230 0.9563 -0.1492
## ci.ub
## intrcpt 0.7058 .
## scale(Moisture_loss_in_percent) 0.1577
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(moisture_model)## R2_marginal R2_conditional
## 3.603676e-05 5.972286e-01
# Plot
dat.moisture <- filter(dat, Moisture_loss_in_percent != "NA")
plot_continuous(dat.moisture, moisture_model, dat.moisture$Moisture_loss_in_percent,
"Percentage of moisture loss")save(category_model, PFAS_model, temp_model, time_model, volume_model, volume0_model,
moisture_model, file = here("Rdata", "single_mod_models.RData")) # Save modelsNA for the dry cooking category# Testing cooking categories
full_model <- rma.mv(yi = lnRR, V = VCV_lnRR, mods = ~1 + Cooking_Category + scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)),
random = list(~1 | Study_ID, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
test = "t", data = dat, sparse = TRUE)
# btt = c(1:3)) # testing the significance of cooking category - testing first
# 3 regression coefficients)
summary(full_model)##
## Multivariate Meta-Analysis Model (k = 392; method: REML)
##
## logLik Deviance AIC BIC AICc
## -436.4870 872.9740 892.9740 932.5324 893.5607
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.3849 0.6204 7 no Study_ID
## sigma^2.2 0.1631 0.4039 26 no Species_common
## sigma^2.3 0.1166 0.3415 18 no PFAS_type
## sigma^2.4 0.4024 0.6343 392 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 386) = 5195.2278, p-val < .0001
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 386) = 9.6991, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.8418 0.3038 -2.7705 386 0.0059
## Cooking_Categoryoil-based 0.1253 0.1634 0.7670 386 0.4435
## scale(Temperature_in_Celsius) -0.3401 0.1274 -2.6689 386 0.0079
## scale(Length_cooking_time_in_s) -0.3344 0.0564 -5.9317 386 <.0001
## scale(PFAS_carbon_chain) 0.0602 0.0777 0.7740 386 0.4394
## scale(log(Ratio_liquid_fish)) -0.8064 0.1684 -4.7889 386 <.0001
## ci.lb ci.ub
## intrcpt -1.4392 -0.2444 **
## Cooking_Categoryoil-based -0.1959 0.4465
## scale(Temperature_in_Celsius) -0.5906 -0.0895 **
## scale(Length_cooking_time_in_s) -0.4453 -0.2236 ***
## scale(PFAS_carbon_chain) -0.0927 0.2130
## scale(log(Ratio_liquid_fish)) -1.1375 -0.4753 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model)## R2_marginal R2_conditional
## 0.3615718 0.7592435
save(full_model, file = here("Rdata", "full_model.RData"))0 for the dry cooking categoryfull_model0 <- rma.mv(yi = lnRR, V = VCV_lnRR, mods = ~1 + Cooking_Category + scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish_0 +
1)), random = list(~1 | Study_ID, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
test = "t", data = dat, sparse = TRUE)
# btt = c(1:3)) # testing the significance of cooking category - testing first
# 3 regression coefficients)
summary(full_model0)##
## Multivariate Meta-Analysis Model (k = 439; method: REML)
##
## logLik Deviance AIC BIC AICc
## -461.7490 923.4980 945.4980 990.2507 946.1266
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.3880 0.6229 7 no Study_ID
## sigma^2.2 0.1726 0.4154 26 no Species_common
## sigma^2.3 0.1285 0.3584 18 no PFAS_type
## sigma^2.4 0.3523 0.5936 439 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 432) = 5297.5498, p-val < .0001
##
## Test of Moderators (coefficients 2:7):
## F(df1 = 6, df2 = 432) = 12.3165, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -2.4067 0.4304 -5.5922 432 <.0001
## Cooking_Categoryoil-based 1.6422 0.3309 4.9636 432 <.0001
## Cooking_Categorywater-based 1.9098 0.3798 5.0289 432 <.0001
## scale(Temperature_in_Celsius) 0.0008 0.0993 0.0081 432 0.9936
## scale(Length_cooking_time_in_s) -0.3761 0.0502 -7.4857 432 <.0001
## scale(PFAS_carbon_chain) 0.0619 0.0787 0.7872 432 0.4316
## scale(log(Ratio_liquid_fish_0 + 1)) -0.8513 0.1463 -5.8194 432 <.0001
## ci.lb ci.ub
## intrcpt -3.2525 -1.5608 ***
## Cooking_Categoryoil-based 0.9919 2.2925 ***
## Cooking_Categorywater-based 1.1634 2.6563 ***
## scale(Temperature_in_Celsius) -0.1944 0.1960
## scale(Length_cooking_time_in_s) -0.4748 -0.2774 ***
## scale(PFAS_carbon_chain) -0.0927 0.2166
## scale(log(Ratio_liquid_fish_0 + 1)) -1.1388 -0.5638 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model0)## R2_marginal R2_conditional
## 0.4842971 0.8255157
save(full_model0, file = here("Rdata", "full_model.RData"))## Check for collinerarity - seems fine
vif(full_model)##
## Cooking_Categoryoil-based scale(Temperature_in_Celsius)
## 2.1734 3.1954
## scale(Length_cooking_time_in_s) scale(PFAS_carbon_chain)
## 1.0593 1.0001
## scale(log(Ratio_liquid_fish))
## 1.7659
vif(full_model0)##
## Cooking_Categoryoil-based Cooking_Categorywater-based
## 13.3453 13.9870
## scale(Temperature_in_Celsius) scale(Length_cooking_time_in_s)
## 2.1620 1.0820
## scale(PFAS_carbon_chain) scale(log(Ratio_liquid_fish_0 + 1))
## 1.0001 8.7258
dat %>%
select(Temperature_in_Celsius, Length_cooking_time_in_s, PFAS_carbon_chain, Ratio_liquid_fish) %>%
ggpairs() # Estimate correlations between the variablesInspection of the plots highlighted potential significant decreases in PFAS content with increased cooking time and volume of cooking. Hence, here we used emmeans (download from remotes::install_github(“rvlenth/emmeans”, dependencies = TRUE, build_opts = "")) to generate marginalised means at specified values of the different predictors. Such analysis enable the quantification of the mean effect size after controlling for different values of the moderators.
Note that these analyses were not performed separately using full models with Ratio_liquid_fish taken as NA or 0. Indeed, a full model containing the dry cooking category and the liquid ratio would extrapolate predictions for the dry cooking category at the mean liquid ratio; which is incorrect. Therefore, all full models were ran with the data containing NA for the Ratio_liquid_fish of the dry cooking method; and separate models were ran with a data subset only containing the dry cooking method.
# Full model in original units (no z-transformation)
dat$log_Ratio_liquid_fish <- log(dat$Ratio_liquid_fish)
full_model_org_units <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)
# Full model in original units (no z-transformation), but without the 'No
# liquid' data This model will be used for conditional analyses on the volume
# of liquid, where the data without liquid is irrelevant
dat_oil_water <- filter(dat, Cooking_Category != "No liquid")
full_model_org_units_oil_water <- run_model(dat_oil_water, ~-1 + Cooking_Category +
Temperature_in_Celsius + Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)
# Full model in original units (no z-transformation), with Ratio_liquid_fish_0
dat$log_Ratio_liquid_fish0 <- log(dat$Ratio_liquid_fish_0 + 1)
full_model_org_units0 <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish0)
# Data subset only containing the data with the dry cooking method. Here, only
# the cooking time was added because the liquid ratio, cooking temperature, and
# PFAS carbon chain length do not have sufficient variability.
dat_dry <- filter(dat, Cooking_Category == "No liquid")
full_model_org_units_dry <- run_model(dat_dry, ~Length_cooking_time_in_s)
save(full_model_org_units, full_model_org_units_dry, full_model_org_units0, full_model_org_units_oil_water,
file = here("Rdata", "full_models_org_units.RData"))NA for the dry cooking categoryres<-mod_results(model = full_model_org_units, data=dat, group="Study_ID", mod="1")
res$mod_table## name estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -0.7789196 -1.335537 -0.2223021 -2.884713 1.326874
orchard_plot(res, mod="1", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))0 for the dry cooking categoryres0<-mod_results(model = full_model_org_units0, data=dat, group="Study_ID",mod="1")
res0$mod_table## name estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -0.9671186 -1.532244 -0.4019933 -3.050876 1.116639
orchard_plot(res0, mod="1", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))res_cat<-mod_results(full_model_org_units, data = dat, mod = "1", group="Study_ID",by = "Cooking_Category")
res_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt oil-based -0.7389599 -1.304068 -0.1738520 -2.847013 1.369093
## 2 Intrcpt water-based -0.8642736 -1.464073 -0.2644743 -2.981891 1.253343
orchard_plot(res_cat, mod="1", condition.lab="Cooking Category", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))res_dry<-mod_results(full_model_org_units_dry, data = dat, group="Study_ID", mod = "1")
res_dry$mod_table## name estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -0.5514386 -0.9618594 -0.1410178 -1.303323 0.2004461
orchard_plot(res_dry, mod="1", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))Here, we generate estimates at cooking times of 2, 10, and 25 min.
NA for the dry cooking categoryres_cooking_time <-mod_results(full_model_org_units, data = dat, mod = "1", group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
res_cooking_time$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 120 -0.1075236 -0.6921709 0.47712374 -2.220899 2.0058516
## 2 Intrcpt 600 -0.6515799 -1.2068086 -0.09635126 -2.757006 1.4538467
## 3 Intrcpt 1500 -1.6716856 -2.3203032 -1.02306792 -3.803644 0.4602729
orchard_plot(res_cooking_time, mod="1", condition.lab="Cooking time (sec)", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))0 for the dry cooking categoryres_cooking_time0 <-mod_results(full_model_org_units0, data = dat, mod = "1", group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
res_cooking_time0$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 120 -0.1873598 -0.7691693 0.3944497 -2.275704 1.9009842
## 2 Intrcpt 600 -0.7991812 -1.3616798 -0.2366827 -2.882228 1.2838653
## 3 Intrcpt 1500 -1.9463465 -2.5898363 -1.3028567 -4.052708 0.1600147
orchard_plot(res_cooking_time0, mod="1", condition.lab="Cooking time (sec)", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))res_cooking_time_cat <-mod_results(full_model_org_units, data = dat, mod = "Cooking_Category", group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
res_cooking_time_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Oil-based 120 -0.06756386 -0.6636572 0.52852947 -2.184134 2.0490064
## 2 Water-based 120 -0.19287754 -0.8119285 0.42617344 -2.316028 1.9302726
## 3 Oil-based 600 -0.61162020 -1.1760311 -0.04720929 -2.719487 1.4962465
## 4 Water-based 600 -0.73693388 -1.3340923 -0.13977542 -2.853804 1.3799366
## 5 Oil-based 1500 -1.63172584 -2.2835862 -0.97986544 -3.764673 0.5012215
## 6 Water-based 1500 -1.75703952 -2.4512846 -1.06279446 -3.903320 0.3892406
orchard_plot(res_cooking_time_cat, mod="1", condition.lab="Cooking time (sec)", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))res_cooking_time_dry <-mod_results(full_model_org_units_dry, data = dat, group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
res_cooking_time_dry$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 120 0.3156080 -0.2261650 0.8573811 -0.5152972 1.1465132
## 2 Intrcpt 600 -0.3704881 -0.7962768 0.0553005 -1.1308705 0.3898943
## 3 Intrcpt 1500 -1.6569185 -2.1485822 -1.1652548 -2.4560547 -0.8577823
orchard_plot(res_cooking_time_dry, mod="1", condition.lab="Cooking time (sec)", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left", k.pos="left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))NA for the dry cooking categoryHere, we generate marginalised estimates at volumes of liquid of ~0.1mL/g of tissue, ~10 ml/g of tissue, or 45 mL/g of tissue. We did not look at the means for different cooking categories because they are inherently different in the volume of liquid used. We also only used the data on oil and water because the “No liquid” category is not relevant for this analysis when considering Ratio_liquid_fish as NA.
res_volume<-mod_results(full_model_org_units_oil_water, data = dat_oil_water, mod = "1", group="Study_ID",at = list(log_Ratio_liquid_fish = c(log(0.1), log(10), log(45))), by = "log_Ratio_liquid_fish")
res_volume$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -2.302585 -0.08071963 -0.6981955 0.5367562 -2.203411 2.0419717
## 2 Intrcpt 2.302585 -1.24833297 -1.8434562 -0.6532098 -3.364630 0.8679642
## 3 Intrcpt 3.806662 -1.62968280 -2.2966824 -0.9626832 -3.767305 0.5079399
orchard_plot(res_volume, mod="1", condition.lab="ln(Liquid volume to tissue sample ratio) (mL/g)", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))Here, we generate marginalised estimates at volumes of liquid of 0mL/g of tissue (dry cooking), ~10 ml/g of tissue, or 45 mL/g of tissue.
res_volume0 <- mod_results(full_model_org_units0, data = dat, mod = "1", group="Study_ID",at = list(log_Ratio_liquid_fish0 = c(0, log(10 + 1), log(45 + 1))), by = "log_Ratio_liquid_fish0")
res_volume0$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 0.000000 -0.07323844 -0.6797402 0.5332634 -2.168596 2.02211899
## 2 Intrcpt 2.397895 -1.37530561 -1.9735175 -0.7770937 -3.468279 0.71766735
## 3 Intrcpt 3.828641 -2.15220674 -2.8844247 -1.4199888 -4.287346 -0.01706707
orchard_plot(res_volume0, mod="1", condition.lab="ln(Liquid volume to tissue sample ratio) (mL/g)", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))NA for the dry cooking categoryHere, we generate marginalized estimates for PFAS of 3, 6, and 12 carbon chains
res_PFAS<-mod_results(full_model_org_units, data = dat, mod = "1", group="Study_ID",at = list(PFAS_carbon_chain= c(3, 6, 12)), by = "PFAS_carbon_chain")
res_PFAS$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 3 -0.9358884 -1.621123 -0.2506540 -3.079271 1.207494
## 2 Intrcpt 6 -0.8579343 -1.449948 -0.2659204 -2.973359 1.257491
## 3 Intrcpt 12 -0.7020262 -1.291611 -0.1124411 -2.816773 1.412720
orchard_plot(res_PFAS, mod="1", condition.lab="PFAS carbon chain", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))0 for the dry cooking categoryres_PFAS0<-mod_results(full_model_org_units0, data = dat, mod = "1", group="Study_ID", at = list(PFAS_carbon_chain= c(3, 6, 12)), by = "PFAS_carbon_chain")
res_PFAS0$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 3 -1.1257163 -1.821397 -0.4300358 -3.248603 0.9971708
## 2 Intrcpt 6 -1.0454730 -1.646720 -0.4442264 -3.139315 1.0483693
## 3 Intrcpt 12 -0.8849865 -1.482788 -0.2871855 -2.977842 1.2078691
orchard_plot(res_PFAS0, mod="1", condition.lab="PFAS carbon chain", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))res_PFAS_cat<-mod_results(full_model_org_units, data = dat, mod = "Cooking_Category", group="Study_ID", at = list(PFAS_carbon_chain= c(3, 6, 12)), by = "PFAS_carbon_chain")
res_PFAS_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Oil-based 3 -0.8959286 -1.587770 -0.20408714 -3.041433 1.249575
## 2 Water-based 3 -1.0212423 -1.742624 -0.29986114 -3.176453 1.133968
## 3 Oil-based 6 -0.8179746 -1.417800 -0.21814937 -2.935599 1.299650
## 4 Water-based 6 -0.9432883 -1.576434 -0.31014215 -3.070591 1.184014
## 5 Oil-based 12 -0.6620665 -1.259848 -0.06428489 -2.779113 1.454980
## 6 Water-based 12 -0.7873801 -1.417540 -0.15722010 -2.913796 1.339036
orchard_plot(res_PFAS_cat, mod="1", condition.lab="PFAS carbon chain", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))Here, we investigated whether the effect of the continuous moderators on lnRR vary depending on the cooking category. Hence, we performed subset analyses for each cooking category.
oil_dat <- filter(dat, Cooking_Category == "oil-based")full_model_oil <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)))
summary(full_model_oil)##
## Multivariate Meta-Analysis Model (k = 267; method: REML)
##
## logLik Deviance AIC BIC AICc
## -178.0833 356.1666 374.1666 406.2817 374.8809
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1101 0.3318 6 no Study_ID
## sigma^2.2 0.0254 0.1594 19 no Species_common
## sigma^2.3 0.0452 0.2127 17 no PFAS_type
## sigma^2.4 0.1269 0.3562 267 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 262) = 980.4322, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 262) = 17.8128, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5678 0.1764 -3.2186 262 0.0015
## scale(Temperature_in_Celsius) -0.0915 0.1186 -0.7717 262 0.4410
## scale(Length_cooking_time_in_s) -0.3799 0.0472 -8.0510 262 <.0001
## scale(PFAS_carbon_chain) 0.1270 0.0597 2.1265 262 0.0344
## scale(log(Ratio_liquid_fish)) -0.2136 0.1971 -1.0839 262 0.2794
## ci.lb ci.ub
## intrcpt -0.9151 -0.2204 **
## scale(Temperature_in_Celsius) -0.3251 0.1420
## scale(Length_cooking_time_in_s) -0.4729 -0.2870 ***
## scale(PFAS_carbon_chain) 0.0094 0.2446 *
## scale(log(Ratio_liquid_fish)) -0.6018 0.1745
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_oil)## R2_marginal R2_conditional
## 0.4575798 0.7762420
save(full_model_oil, file = here("Rdata", "full_model_oil.RData"))0 for the dry cooking categoryfull_model_oil0 <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish_0 + 1)))
summary(full_model_oil0)##
## Multivariate Meta-Analysis Model (k = 267; method: REML)
##
## logLik Deviance AIC BIC AICc
## -176.9888 353.9777 371.9777 404.0928 372.6920
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1088 0.3299 6 no Study_ID
## sigma^2.2 0.0226 0.1505 19 no Species_common
## sigma^2.3 0.0474 0.2177 17 no PFAS_type
## sigma^2.4 0.1263 0.3554 267 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 262) = 977.2195, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 262) = 18.3292, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5729 0.1751 -3.2713 262 0.0012
## scale(Temperature_in_Celsius) -0.0151 0.0807 -0.1870 262 0.8518
## scale(Length_cooking_time_in_s) -0.3808 0.0470 -8.1006 262 <.0001
## scale(PFAS_carbon_chain) 0.1272 0.0604 2.1063 262 0.0361
## scale(log(Ratio_liquid_fish_0 + 1)) -0.3160 0.1783 -1.7721 262 0.0775
## ci.lb ci.ub
## intrcpt -0.9177 -0.2281 **
## scale(Temperature_in_Celsius) -0.1741 0.1439
## scale(Length_cooking_time_in_s) -0.4734 -0.2883 ***
## scale(PFAS_carbon_chain) 0.0083 0.2462 *
## scale(log(Ratio_liquid_fish_0 + 1)) -0.6671 0.0351 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_oil0)## R2_marginal R2_conditional
## 0.5677215 0.8210732
save(full_model_oil0, file = here("Rdata", "full_model_oil0.RData"))water_dat <- filter(dat, Cooking_Category == "water-based")full_model_water <- run_model(water_dat, ~scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model_water)##
## Multivariate Meta-Analysis Model (k = 125; method: REML)
##
## logLik Deviance AIC BIC AICc
## -183.2158 366.4317 382.4317 404.7980 383.7174
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.6052 0.7780 6 no Study_ID
## sigma^2.2 0.0000 0.0001 19 no Species_common
## sigma^2.3 0.5028 0.7091 16 no PFAS_type
## sigma^2.4 0.9080 0.9529 125 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 121) = 3991.9601, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 121) = 4.2885, p-val = 0.0065
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.2870 0.4106 -3.1347 121 0.0022
## scale(Length_cooking_time_in_s) -0.3766 0.1569 -2.3999 121 0.0179
## scale(PFAS_carbon_chain) -0.0512 0.1737 -0.2947 121 0.7687
## scale(log(Ratio_liquid_fish)) -0.6326 0.2462 -2.5700 121 0.0114
## ci.lb ci.ub
## intrcpt -2.0997 -0.4742 **
## scale(Length_cooking_time_in_s) -0.6873 -0.0659 *
## scale(PFAS_carbon_chain) -0.3950 0.2926
## scale(log(Ratio_liquid_fish)) -1.1200 -0.1453 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_water)## R2_marginal R2_conditional
## 0.2108734 0.6445865
0 for the dry cooking categoryfull_model_water0 <- run_model(water_dat, ~scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
summary(full_model_water0)##
## Multivariate Meta-Analysis Model (k = 125; method: REML)
##
## logLik Deviance AIC BIC AICc
## -183.2093 366.4185 382.4185 404.7848 383.7042
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5962 0.7722 6 no Study_ID
## sigma^2.2 0.0000 0.0001 19 no Species_common
## sigma^2.3 0.5036 0.7097 16 no PFAS_type
## sigma^2.4 0.9078 0.9528 125 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 121) = 3990.7659, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 121) = 4.3192, p-val = 0.0062
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.2802 0.4078 -3.1390 121 0.0021
## scale(Length_cooking_time_in_s) -0.3716 0.1568 -2.3708 121 0.0193
## scale(PFAS_carbon_chain) -0.0516 0.1737 -0.2968 121 0.7672
## scale(log(Ratio_liquid_fish_0 + 1)) -0.6191 0.2392 -2.5885 121 0.0108
## ci.lb ci.ub
## intrcpt -2.0876 -0.4728 **
## scale(Length_cooking_time_in_s) -0.6820 -0.0613 *
## scale(PFAS_carbon_chain) -0.3955 0.2924
## scale(log(Ratio_liquid_fish_0 + 1)) -1.0925 -0.1456 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_water0)## R2_marginal R2_conditional
## 0.2068068 0.6413383
In our data set, the studies using steaming-based cooking were considered to have an unknown (i.e. NA) because of the difficulty to assess how much liquid gets in contact with the products. Here, we provide an analysis to compare steaming with other water-based cooking categories
water_dat$steamed<-ifelse(water_dat$Cooking_method=="Steaming","steamed","other") # create a dummy variable to differentiate "steaming" with other types of water-based cooking
full_model_water_steamed <- run_model(water_dat, ~ -1 + # without intercept
steamed +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain)) # In this case, we need to remove the Ratio liquid fish from the model. Otherwise, it would remove observations where the liquid volume was unknown.
summary(full_model_water_steamed)##
## Multivariate Meta-Analysis Model (k = 144; method: REML)
##
## logLik Deviance AIC BIC AICc
## -214.9470 429.8939 445.8939 469.4270 446.9931
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.6927 0.8323 8 no Study_ID
## sigma^2.2 0.0757 0.2752 23 no Species_common
## sigma^2.3 0.2511 0.5011 16 no PFAS_type
## sigma^2.4 0.9426 0.9709 144 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 140) = 4479.1069, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 140) = 1.8812, p-val = 0.1170
##
## Model Results:
##
## estimate se tval df pval
## steamedother -0.6962 0.3836 -1.8149 140 0.0717
## steamedsteamed -0.5425 0.4468 -1.2143 140 0.2267
## scale(Length_cooking_time_in_s) -0.3098 0.1578 -1.9638 140 0.0515
## scale(PFAS_carbon_chain) -0.0506 0.1360 -0.3720 140 0.7105
## ci.lb ci.ub
## steamedother -1.4545 0.0622 .
## steamedsteamed -1.4258 0.3408
## scale(Length_cooking_time_in_s) -0.6218 0.0021 .
## scale(PFAS_carbon_chain) -0.3195 0.2183
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Contrast between steamed and non-steamed
full_model_water_steamed_cont <- run_model(water_dat,
~ steamed + # with intercept
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain))
summary(full_model_water_steamed_cont)##
## Multivariate Meta-Analysis Model (k = 144; method: REML)
##
## logLik Deviance AIC BIC AICc
## -214.9470 429.8939 445.8939 469.4270 446.9931
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.6927 0.8323 8 no Study_ID
## sigma^2.2 0.0757 0.2752 23 no Species_common
## sigma^2.3 0.2511 0.5011 16 no PFAS_type
## sigma^2.4 0.9426 0.9709 144 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 140) = 4479.1069, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 140) = 1.3932, p-val = 0.2474
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.6962 0.3836 -1.8149 140 0.0717
## steamedsteamed 0.1537 0.4008 0.3834 140 0.7020
## scale(Length_cooking_time_in_s) -0.3098 0.1578 -1.9638 140 0.0515
## scale(PFAS_carbon_chain) -0.0506 0.1360 -0.3720 140 0.7105
## ci.lb ci.ub
## intrcpt -1.4545 0.0622 .
## steamedsteamed -0.6387 0.9461
## scale(Length_cooking_time_in_s) -0.6218 0.0021 .
## scale(PFAS_carbon_chain) -0.3195 0.2183
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(full_model_water, full_model_water_steamed, full_model_water_steamed_cont, file = here("Rdata", "full_model_water.RData"))dry_dat <- filter(dat, Cooking_Category == "No liquid")full_model_dry <- run_model(dry_dat, ~scale(Length_cooking_time_in_s))
summary(full_model_dry)##
## Multivariate Meta-Analysis Model (k = 47; method: REML)
##
## logLik Deviance AIC BIC AICc
## -11.5095 23.0189 33.0189 42.0522 34.5574
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0000 0.0000 1 yes Study_ID
## sigma^2.2 0.0023 0.0476 8 no Species_common
## sigma^2.3 0.0730 0.2702 2 no PFAS_type
## sigma^2.4 0.0226 0.1503 47 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 45) = 96.7492, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 45) = 38.0048, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.7902 0.2000 -3.9518 45 0.0003 -1.1929
## scale(Length_cooking_time_in_s) -0.3519 0.0571 -6.1648 45 <.0001 -0.4669
## ci.ub
## intrcpt -0.3875 ***
## scale(Length_cooking_time_in_s) -0.2369 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_dry)## R2_marginal R2_conditional
## 0.5586372 0.8981181
save(full_model_dry, file = here("Rdata", "full_model_dry.RData")) oil_dat <- filter(dat, Cooking_Category=="oil-based")
water_dat <- filter(dat, Cooking_Category=="water-based")
dry_dat <- filter(dat, Cooking_Category=="No liquid")
oil_dat_time<-filter(oil_dat, Length_cooking_time_in_s!="NA")
water_dat_time<-filter(water_dat, Length_cooking_time_in_s!="NA")
dry_dat_time<-filter(dry_dat, Length_cooking_time_in_s!="NA")
model_oil_time<-run_model(oil_dat_time, ~Length_cooking_time_in_s)
model_water_time<-run_model(water_dat_time, ~Length_cooking_time_in_s)
model_dry_time<-run_model(dry_dat_time, ~Length_cooking_time_in_s)
pred_oil_time<-predict.rma(model_oil_time)
pred_water_time<-predict.rma(model_water_time)
pred_dry_time<-predict.rma(model_dry_time)
oil_dat_time<-mutate(oil_dat_time,
ci.lb = pred_oil_time$ci.lb, # lower bound of the confidence interval for oil
ci.ub = pred_oil_time$ci.ub, # upper bound of the confidence interval for oil
fit = pred_oil_time$pred) # regression line for oil
water_dat_time<-mutate(water_dat_time,
ci.lb = pred_water_time$ci.lb, # lower bound of the confidence interval for water
ci.ub = pred_water_time$ci.ub, # upper bound of the confidence interval for water
fit = pred_water_time$pred) # regression line for water
dry_dat_time<-mutate(dry_dat_time,
ci.lb = pred_dry_time$ci.lb, # lower bound of the confidence interval for dry
ci.ub = pred_dry_time$ci.ub, # upper bound of the confidence interval for dry
fit = pred_dry_time$pred) # regression line for dryggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=water_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=water_dat_time,aes(y = fit), size = 1.5, col="dodgerblue")+
geom_ribbon(data=oil_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=oil_dat_time,aes(y = fit), size = 1.5, col="goldenrod")+
geom_ribbon(data=dry_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=dry_dat_time,aes(y = fit), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))NA for the dry cooking category##### Oil based
full_model_oil_time<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_time<-predict.rma(full_model_oil_time, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time<-as.data.frame(pred_oil_time)
pred_oil_time$Length_cooking_time_in_s=pred_oil_time$X.Length_cooking_time_in_s
pred_oil_time<-left_join(oil_dat, pred_oil_time, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_water_time<-predict.rma(full_model_water_time, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time<-as.data.frame(pred_water_time)
pred_water_time$Length_cooking_time_in_s=pred_water_time$X.Length_cooking_time_in_s
pred_water_time<-left_join(water_dat, pred_water_time, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))0 for the dry cooking category##### Oil based
full_model_oil_time0<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_time0<-predict.rma(full_model_oil_time0, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time0<-as.data.frame(pred_oil_time0)
pred_oil_time0$Length_cooking_time_in_s=pred_oil_time0$X.Length_cooking_time_in_s
pred_oil_time0<-left_join(oil_dat, pred_oil_time0, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time0<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_water_time0<-predict.rma(full_model_water_time0, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time0<-as.data.frame(pred_water_time0)
pred_water_time0$Length_cooking_time_in_s=pred_water_time0$X.Length_cooking_time_in_s
pred_water_time0<-left_join(water_dat, pred_water_time0, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))oil_dat_vol <- filter(oil_dat, Ratio_liquid_fish != "NA")
water_dat_vol <- filter(water_dat, Ratio_liquid_fish != "NA")
model_oil_vol <- run_model(oil_dat_vol, ~log(Ratio_liquid_fish))
model_water_vol <- run_model(water_dat_vol, ~log(Ratio_liquid_fish))
pred_oil_vol <- predict.rma(model_oil_vol)
pred_water_vol <- predict.rma(model_water_vol)
oil_dat_vol <- mutate(oil_dat_vol, ci.lb = pred_oil_vol$ci.lb, ci.ub = pred_oil_vol$ci.ub,
fit = pred_oil_vol$pred)
water_dat_vol <- mutate(water_dat_vol, ci.lb = pred_water_vol$ci.lb, ci.ub = pred_water_vol$ci.ub,
fit = pred_water_vol$pred)
oil_dat$log_Ratio_liquid_fish <- log(oil_dat$Ratio_liquid_fish)
water_dat$log_Ratio_liquid_fish <- log(water_dat$Ratio_liquid_fish)ggplot(dat, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = water_dat_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = water_dat_vol, aes(y = fit), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = oil_dat_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data = oil_dat_vol, aes(y = fit), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue ratio (mL/g))",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))##### Oil based
full_model_oil_vol <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_oil_vol <- predict.rma(full_model_oil_vol, addx = TRUE, newmods = cbind(0, 0,
0, oil_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol <- as.data.frame(pred_oil_vol)
pred_oil_vol <- pred_oil_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol <- run_model(water_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_water_vol <- predict.rma(full_model_water_vol, addx = TRUE, newmods = cbind(0,
0, water_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol <- as.data.frame(pred_water_vol)
pred_water_vol <- pred_water_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "water-based",
lnRR = 0)
ggplot(dat, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol, aes(y = pred), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data = pred_oil_vol, aes(y = pred), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio (mL/g))",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2)) #### The line doesn't go all the way down for water-based because the highest values are not included in the full modeloil_dat_PFAS <- filter(oil_dat, PFAS_carbon_chain != "NA")
water_dat_PFAS <- filter(water_dat, PFAS_carbon_chain != "NA")
dry_dat_PFAS <- filter(dry_dat, PFAS_carbon_chain != "NA")
model_oil_PFAS <- run_model(oil_dat_PFAS, ~PFAS_carbon_chain)
model_water_PFAS <- run_model(water_dat_PFAS, ~PFAS_carbon_chain)
model_dry_PFAS <- run_model(dry_dat_PFAS, ~PFAS_carbon_chain)
pred_oil_PFAS <- predict.rma(model_oil_PFAS)
pred_water_PFAS <- predict.rma(model_water_PFAS)
pred_dry_PFAS <- predict.rma(model_dry_PFAS)
oil_dat_PFAS <- mutate(oil_dat_PFAS, ci.lb = pred_oil_PFAS$ci.lb, ci.ub = pred_oil_PFAS$ci.ub,
fit = pred_oil_PFAS$pred)
water_dat_PFAS <- mutate(water_dat_PFAS, ci.lb = pred_water_PFAS$ci.lb, ci.ub = pred_water_PFAS$ci.ub,
fit = pred_water_PFAS$pred)
dry_dat_PFAS <- mutate(dry_dat_PFAS, ci.lb = pred_dry_PFAS$ci.lb, ci.ub = pred_dry_PFAS$ci.ub,
fit = pred_dry_PFAS$pred)For some reason the plot doesn’t want to knit, although the script works
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + ggplot(dat,
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + aes(x
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + =
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + PFAS_carbon_chain,
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + y
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + =
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + lnRR,
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + fill
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + =
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + Cooking_Category))
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + +
geom_ribbon(data = dry_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data = dry_dat_PFAS, aes(y = fit), size = 1.5, col = "palegreen3") +
geom_ribbon(data = water_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = water_dat_PFAS, aes(y = fit), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = oil_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data = oil_dat_PFAS, aes(y = fit), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "PFAS carbon chain length",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))NA for the dry cooking category##### Oil based
full_model_oil_PFAS<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_oil_PFAS<-predict.rma(full_model_oil_PFAS, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS<-as.data.frame(pred_oil_PFAS)
pred_oil_PFAS$PFAS_carbon_chain=pred_oil_PFAS$X.PFAS_carbon_chain
pred_oil_PFAS<-left_join(oil_dat, pred_oil_PFAS, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_water_PFAS<-predict.rma(full_model_water_PFAS, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS<-as.data.frame(pred_water_PFAS)
pred_water_PFAS$PFAS_carbon_chain=pred_water_PFAS$X.PFAS_carbon_chain
pred_water_PFAS<-left_join(water_dat, pred_water_PFAS, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))NA for the dry cooking category##### Oil based
full_model_oil_PFAS0<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_PFAS0<-predict.rma(full_model_oil_PFAS0, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS0<-as.data.frame(pred_oil_PFAS0)
pred_oil_PFAS0$PFAS_carbon_chain=pred_oil_PFAS0$X.PFAS_carbon_chain
pred_oil_PFAS0<-left_join(oil_dat, pred_oil_PFAS0, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS0<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish + 1)))
pred_water_PFAS0<-predict.rma(full_model_water_PFAS0, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS0<-as.data.frame(pred_water_PFAS0)
pred_water_PFAS0$PFAS_carbon_chain=pred_water_PFAS0$X.PFAS_carbon_chain
pred_water_PFAS0<-left_join(water_dat, pred_water_PFAS0, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))NA for the dry cooking categoryfunnel(full_model, yaxis = "seinv")funnel(full_model)0 for the dry cooking categoryfunnel(full_model0, yaxis = "seinv")funnel(full_model0)NA for the dry cooking categoryegger_all <- run_model(dat, ~-1 + Cooking_Category + I(sqrt(1/N_tilde)) + scale(Publication_year) +
scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(egger_all)##
## Multivariate Meta-Analysis Model (k = 392; method: REML)
##
## logLik Deviance AIC BIC AICc
## -429.8336 859.6671 883.6671 931.0748 884.5081
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0054 0.0737 7 no Study_ID
## sigma^2.2 0.2026 0.4501 26 no Species_common
## sigma^2.3 0.1179 0.3433 18 no PFAS_type
## sigma^2.4 0.3993 0.6319 392 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 384) = 4824.4395, p-val < .0001
##
## Test of Moderators (coefficients 1:8):
## F(df1 = 8, df2 = 384) = 15.6838, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Cooking_Categoryoil-based -0.6845 0.3608 -1.8969 384 0.0586
## Cooking_Categorywater-based -0.8328 0.3614 -2.3041 384 0.0217
## I(sqrt(1/N_tilde)) 0.1898 0.5958 0.3185 384 0.7503
## scale(Publication_year) 0.4299 0.0832 5.1654 384 <.0001
## scale(Temperature_in_Celsius) -0.3528 0.1233 -2.8620 384 0.0044
## scale(Length_cooking_time_in_s) -0.3354 0.0532 -6.3000 384 <.0001
## scale(PFAS_carbon_chain) 0.0788 0.0778 1.0123 384 0.3121
## scale(log(Ratio_liquid_fish)) -0.9165 0.1458 -6.2844 384 <.0001
## ci.lb ci.ub
## Cooking_Categoryoil-based -1.3940 0.0250 .
## Cooking_Categorywater-based -1.5435 -0.1222 *
## I(sqrt(1/N_tilde)) -0.9817 1.3613
## scale(Publication_year) 0.2663 0.5936 ***
## scale(Temperature_in_Celsius) -0.5951 -0.1104 **
## scale(Length_cooking_time_in_s) -0.4400 -0.2307 ***
## scale(PFAS_carbon_chain) -0.0742 0.2318
## scale(log(Ratio_liquid_fish)) -1.2033 -0.6298 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
egger_n <- run_model(dat, ~I(sqrt(1/N_tilde)))
summary(egger_n)##
## Multivariate Meta-Analysis Model (k = 520; method: REML)
##
## logLik Deviance AIC BIC AICc
## -627.4171 1254.8342 1266.8342 1292.3341 1266.9986
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5858 0.7654 10 no Study_ID
## sigma^2.2 0.1944 0.4409 39 no Species_common
## sigma^2.3 0.0929 0.3048 19 no PFAS_type
## sigma^2.4 0.4697 0.6853 519 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 518) = 7276.0058, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 518) = 0.0153, p-val = 0.9015
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.3629 0.4044 -0.8974 518 0.3699 -1.1574 0.4315
## I(sqrt(1/N_tilde)) 0.0732 0.5916 0.1238 518 0.9015 -1.0890 1.2354
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
NA for the dry cooking categoryegger_all0 <- run_model(dat, ~-1 + Cooking_Category + I(sqrt(1/N_tilde)) + scale(Publication_year) +
scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
summary(egger_all0)##
## Multivariate Meta-Analysis Model (k = 439; method: REML)
##
## logLik Deviance AIC BIC AICc
## -456.4089 912.8178 938.8178 991.6470 939.6928
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1055 0.3249 7 no Study_ID
## sigma^2.2 0.1898 0.4356 26 no Species_common
## sigma^2.3 0.1329 0.3645 18 no PFAS_type
## sigma^2.4 0.3519 0.5932 439 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 430) = 4949.2082, p-val < .0001
##
## Test of Moderators (coefficients 1:9):
## F(df1 = 9, df2 = 430) = 11.2141, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Cooking_CategoryNo liquid -2.2916 0.4426 -5.1781 430 <.0001
## Cooking_Categoryoil-based -0.6886 0.3821 -1.8024 430 0.0722
## Cooking_Categorywater-based -0.4277 0.3956 -1.0811 430 0.2802
## I(sqrt(1/N_tilde)) 0.0387 0.5962 0.0649 430 0.9483
## scale(Publication_year) 0.3581 0.1225 2.9224 430 0.0037
## scale(Temperature_in_Celsius) 0.0109 0.0975 0.1116 430 0.9112
## scale(Length_cooking_time_in_s) -0.3703 0.0491 -7.5343 430 <.0001
## scale(PFAS_carbon_chain) 0.0723 0.0796 0.9077 430 0.3645
## scale(log(Ratio_liquid_fish_0 + 1)) -0.8315 0.1348 -6.1665 430 <.0001
## ci.lb ci.ub
## Cooking_CategoryNo liquid -3.1615 -1.4218 ***
## Cooking_Categoryoil-based -1.4396 0.0623 .
## Cooking_Categorywater-based -1.2053 0.3499
## I(sqrt(1/N_tilde)) -1.1331 1.2104
## scale(Publication_year) 0.1173 0.5990 **
## scale(Temperature_in_Celsius) -0.1808 0.2025
## scale(Length_cooking_time_in_s) -0.4669 -0.2737 ***
## scale(PFAS_carbon_chain) -0.0842 0.2287
## scale(log(Ratio_liquid_fish_0 + 1)) -1.0966 -0.5665 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(egger_all, egger_all0, egger_n, file = here("Rdata", "egger_regressions.RData"))pub_year <- run_model(dat, ~Publication_year)
summary(pub_year)##
## Multivariate Meta-Analysis Model (k = 520; method: REML)
##
## logLik Deviance AIC BIC AICc
## -625.7997 1251.5994 1263.5994 1289.0992 1263.7638
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5594 0.7479 10 no Study_ID
## sigma^2.2 0.1893 0.4351 39 no Species_common
## sigma^2.3 0.0930 0.3050 19 no PFAS_type
## sigma^2.4 0.4695 0.6852 519 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 518) = 7239.5633, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 518) = 1.3088, p-val = 0.2531
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -166.0682 144.8762 -1.1463 518 0.2522 -450.6853
## Publication_year 0.0822 0.0718 1.1440 518 0.2531 -0.0589
## ci.ub
## intrcpt 118.5489
## Publication_year 0.2233
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat, pub_year, dat$Publication_year, "Publication year") ##
Here, we iteratively removed one study at the time and investigated how it affects the overall mean. Removing one specific study particularly modifies the estimate, but none of these models show a significant overall difference in PFAS concentration with cooking.
dat$Study_ID<-as.factor(dat$Study_ID)
dat<-as.data.frame(dat) # Only work with a dataframe
VCV_matrix<-list() # will need new VCV matrices because the sample size will be iteratively reduced
Leave1studyout<-list() # create a list that will host the results of each model
for(i in 1:length(levels(dat$Study_ID))){ # N models = N studies
VCV_matrix[[i]]<-make_VCV_matrix(dat[dat$Study_ID != levels(dat$Study_ID)[i], ], V="var_lnRR", cluster="Cohort_ID", obs="Effect_ID") # Create a new VCV matrix for each new model
Leave1studyout[[i]] <- rma.mv(yi = lnRR, V = VCV_matrix[[i]], # Same model structure as all the models we fitted
random = list(~1|Study_ID,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
test = "t",
data = dat[dat$Study_ID != levels(dat$Study_ID)[i], ]) # Generate a new model for each new data (iterative removal of one study at a time)
}
# The output is a list so we need to summarise the coefficients of all the models performed
results.Leave1studyout<-as.data.frame(cbind(
sapply(Leave1studyout, function(x) summary(x)$beta), # extract the beta coefficient from all models
sapply(Leave1studyout, function(x) summary(x)$se), # extract the standard error from all models
sapply(Leave1studyout, function(x) summary(x)$zval), # extract the z value from all models
sapply(Leave1studyout, function(x) summary(x)$pval), # extract the p value from all models
sapply(Leave1studyout, function(x) summary(x)$ci.lb), # extract the lower confidence interval for all models
sapply(Leave1studyout, function(x) summary(x)$ci.ub))) # extract the upper confidence interval for all models
colnames(results.Leave1studyout)=c("Estimate", "SE", "zval", "pval", "ci.lb", "ci.ub") # change column names
kable(results.Leave1studyout)%>% kable_styling("striped", position="left") %>% scroll_box(width="100%", height="500px") # Table of the results from all models| Estimate | SE | zval | pval | ci.lb | ci.ub |
|---|---|---|---|---|---|
| -0.3369554 | 0.3022023 | -1.1149996 | 0.2653686 | -0.9306508 | 0.2567400 |
| -0.4089704 | 0.3039753 | -1.3454067 | 0.1790918 | -1.0061683 | 0.1882275 |
| -0.4277301 | 0.3268956 | -1.3084610 | 0.1914876 | -1.0704282 | 0.2149679 |
| -0.0661101 | 0.2182793 | -0.3028694 | 0.7621171 | -0.4949825 | 0.3627623 |
| -0.3383542 | 0.3077068 | -1.0995992 | 0.2720500 | -0.9429510 | 0.2662427 |
| -0.2493219 | 0.2960366 | -0.8421996 | 0.4000830 | -0.8309652 | 0.3323214 |
| -0.3439866 | 0.3058527 | -1.1246807 | 0.2612576 | -0.9448842 | 0.2569109 |
| -0.2252427 | 0.3037371 | -0.7415712 | 0.4588794 | -0.8227681 | 0.3722827 |
| -0.4147335 | 0.3062829 | -1.3540865 | 0.1763433 | -1.0165482 | 0.1870811 |
| -0.4869562 | 0.2835366 | -1.7174366 | 0.0865681 | -1.0441343 | 0.0702219 |
dat %>% group_by(Author_year, Study_ID) %>% summarise(mean=mean(lnRR)) # Study F005 (DelGobbo_2008) has much lower effect sizes than the others. ## # A tibble: 10 x 3
## # Groups: Author_year [10]
## Author_year Study_ID mean
## <chr> <fct> <dbl>
## 1 Alves_2017 F001 -0.0773
## 2 Barbosa_2018 F002 0.198
## 3 Bhavsar_2014 F003 0.153
## 4 DelGobbo_2008 F005 -2.00
## 5 Hu_2020 F006 -0.134
## 6 Kim_2020 F007 -0.886
## 7 Luo_2019 F008 -0.161
## 8 Sungur_2019 F010 -0.893
## 9 Taylor_2019 F011 0.221
## 10 Vassiliadou_2015 F013 0.672
Study_ID F005 (Del Gobbo et al. 2008)dat.sens <- filter(dat, Author_year != "DelGobbo_2008")
dat.sens <- as.data.frame(dat.sens) # convert data set into a data frame to calculate VCV matrix
VCV_lnRR.sens <- make_VCV_matrix(dat.sens, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens <- rma.mv(lnRR, VCV_lnRR.sens, mods = ~Length_cooking_time_in_s, random = list(~1 |
Study_ID, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID), test = "t", data = dat.sens)
summary(mod.sens)##
## Multivariate Meta-Analysis Model (k = 438; method: REML)
##
## logLik Deviance AIC BIC AICc
## -265.6744 531.3489 543.3489 567.8147 543.5447
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.2080 0.4560 8 no Study_ID
## sigma^2.2 0.0231 0.1518 22 no Species_common
## sigma^2.3 0.0896 0.2993 18 no PFAS_type
## sigma^2.4 0.0863 0.2937 438 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 436) = 2029.2769, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 436) = 105.2649, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.5871 0.2071 2.8344 436 0.0048 0.1800
## Length_cooking_time_in_s -0.0012 0.0001 -10.2599 436 <.0001 -0.0014
## ci.ub
## intrcpt 0.9942 **
## Length_cooking_time_in_s -0.0009 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dat.time.sens <- filter(dat.sens, Length_cooking_time_in_s != "NA")
plot_continuous(dat.time.sens, mod.sens, dat.time.sens$Length_cooking_time_in_s,
"Cooking time (s)") # The relationship with cooking time appears even strongeroil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
dry_dat.sens <- filter(dat.sens, Cooking_Category == "No liquid")
oil_dat_time.sens <- filter(oil_dat.sens, Length_cooking_time_in_s != "NA")
water_dat_time.sens <- filter(water_dat.sens, Length_cooking_time_in_s != "NA")
dry_dat_time.sens <- filter(dry_dat.sens, Length_cooking_time_in_s != "NA")
model_oil_time.sens <- run_model(oil_dat_time.sens, ~Length_cooking_time_in_s)
model_water_time.sens <- run_model(water_dat_time.sens, ~Length_cooking_time_in_s)
model_dry_time.sens <- run_model(dry_dat_time.sens, ~Length_cooking_time_in_s)
summary(model_oil_time.sens)##
## Multivariate Meta-Analysis Model (k = 267; method: REML)
##
## logLik Deviance AIC BIC AICc
## -125.0666 250.1332 262.1332 283.6116 262.4588
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.2752 0.5246 5 no Study_ID
## sigma^2.2 0.0151 0.1227 15 no Species_common
## sigma^2.3 0.1343 0.3664 17 no PFAS_type
## sigma^2.4 0.0373 0.1932 267 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 265) = 733.5770, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 265) = 97.7593, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.4337 0.2777 1.5617 265 0.1196 -0.1131
## Length_cooking_time_in_s -0.0015 0.0002 -9.8873 265 <.0001 -0.0018
## ci.ub
## intrcpt 0.9805
## Length_cooking_time_in_s -0.0012 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_water_time.sens)##
## Multivariate Meta-Analysis Model (k = 124; method: REML)
##
## logLik Deviance AIC BIC AICc
## -103.2167 206.4334 218.4334 235.2576 219.1639
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1330 0.3647 7 no Study_ID
## sigma^2.2 0.0000 0.0000 17 no Species_common
## sigma^2.3 0.0896 0.2994 16 no PFAS_type
## sigma^2.4 0.1749 0.4182 124 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 122) = 1074.9428, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 122) = 22.4390, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.6465 0.2583 2.5034 122 0.0136 0.1353
## Length_cooking_time_in_s -0.0012 0.0002 -4.7370 122 <.0001 -0.0017
## ci.ub
## intrcpt 1.1577 *
## Length_cooking_time_in_s -0.0007 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_dry_time.sens)##
## Multivariate Meta-Analysis Model (k = 47; method: REML)
##
## logLik Deviance AIC BIC AICc
## -11.5095 23.0189 33.0189 42.0522 34.5574
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0000 0.0000 1 yes Study_ID
## sigma^2.2 0.0023 0.0476 8 no Species_common
## sigma^2.3 0.0730 0.2702 2 no PFAS_type
## sigma^2.4 0.0226 0.1503 47 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 45) = 96.7492, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 45) = 38.0048, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.4871 0.2883 1.6894 45 0.0981 -0.0936
## Length_cooking_time_in_s -0.0014 0.0002 -6.1648 45 <.0001 -0.0019
## ci.ub
## intrcpt 1.0679 .
## Length_cooking_time_in_s -0.0010 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_time.sens <- predict.rma(model_oil_time.sens)
pred_water_time.sens <- predict.rma(model_water_time.sens)
pred_dry_time.sens <- predict.rma(model_dry_time.sens)
oil_dat_time.sens <- mutate(oil_dat_time.sens, ci.lb = pred_oil_time.sens$ci.lb,
ci.ub = pred_oil_time.sens$ci.ub, fit = pred_oil_time.sens$pred)
water_dat_time.sens <- mutate(water_dat_time.sens, ci.lb = pred_water_time.sens$ci.lb,
ci.ub = pred_water_time.sens$ci.ub, fit = pred_water_time.sens$pred)
dry_dat_time.sens <- mutate(dry_dat_time.sens, ci.lb = pred_dry_time.sens$ci.lb,
ci.ub = pred_dry_time.sens$ci.ub, fit = pred_dry_time.sens$pred)For some reason the plot doesn’t want to knit, although the script works
# Actual plot
ggplot(dat.sens, aes(x = Length_cooking_time_in_s, y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = water_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = water_dat_time.sens, aes(y = fit), size = 1.5,
col = "dodgerblue") + col = "dodgerblue") +
geom_ribbon(data = oil_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = oil_dat_time.sens, aes(y = fit), size = 1.5,
col = "goldenrod") + col = "goldenrod") +
geom_ribbon(data = dry_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.25) + geom_line(data = dry_dat_time.sens, aes(y = fit), size = 1.5,
col = "palegreen3") + col = "palegreen3") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "Cooking time (s)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))##### Oil based
full_model_oil_time.sens<- run_model(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model_oil_time.sens)##
## Multivariate Meta-Analysis Model (k = 261; method: REML)
##
## logLik Deviance AIC BIC AICc
## -104.9549 209.9099 227.9099 259.8165 228.6416
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1405 0.3749 5 no Study_ID
## sigma^2.2 0.0186 0.1363 15 no Species_common
## sigma^2.3 0.1042 0.3228 17 no PFAS_type
## sigma^2.4 0.0270 0.1642 261 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 256) = 534.5877, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 256) = 27.8604, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.4940 0.2235 2.2101 256 0.0280 0.0538
## scale(Temperature_in_Celsius) -0.1098 0.1280 -0.8577 256 0.3919 -0.3619
## Length_cooking_time_in_s -0.0015 0.0001 -10.1583 256 <.0001 -0.0018
## scale(PFAS_carbon_chain) 0.1412 0.0705 2.0034 256 0.0462 0.0024
## scale(log(Ratio_liquid_fish)) -0.2153 0.2296 -0.9376 256 0.3493 -0.6673
## ci.ub
## intrcpt 0.9342 *
## scale(Temperature_in_Celsius) 0.1423
## Length_cooking_time_in_s -0.0012 ***
## scale(PFAS_carbon_chain) 0.2800 *
## scale(log(Ratio_liquid_fish)) 0.2368
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_time.sens<-predict.rma(full_model_oil_time.sens, addx=TRUE, newmods=cbind(0,oil_dat.sens$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time.sens<-as.data.frame(pred_oil_time.sens)
pred_oil_time.sens$Length_cooking_time_in_s=pred_oil_time.sens$X.Length_cooking_time_in_s
pred_oil_time.sens<-left_join(oil_dat.sens, pred_oil_time.sens, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time.sens<- run_model(water_dat.sens, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model_water_time.sens)##
## Multivariate Meta-Analysis Model (k = 105; method: REML)
##
## logLik Deviance AIC BIC AICc
## -61.2148 122.4297 138.4297 159.3506 139.9949
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1609 0.4012 5 no Study_ID
## sigma^2.2 0.0000 0.0000 13 no Species_common
## sigma^2.3 0.1113 0.3336 16 no PFAS_type
## sigma^2.4 0.0617 0.2484 105 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 101) = 318.8891, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 101) = 15.8249, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.3299 0.2783 1.1855 101 0.2386 -0.2221
## Length_cooking_time_in_s -0.0013 0.0002 -6.2118 101 <.0001 -0.0018
## scale(PFAS_carbon_chain) 0.1669 0.0786 2.1221 101 0.0363 0.0109
## scale(log(Ratio_liquid_fish)) -0.2843 0.1581 -1.7977 101 0.0752 -0.5979
## ci.ub
## intrcpt 0.8818
## Length_cooking_time_in_s -0.0009 ***
## scale(PFAS_carbon_chain) 0.3228 *
## scale(log(Ratio_liquid_fish)) 0.0294 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_water_time.sens<-predict.rma(full_model_water_time.sens, addx=TRUE, newmods=cbind(water_dat.sens$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time.sens<-as.data.frame(pred_water_time.sens)
pred_water_time.sens$Length_cooking_time_in_s=pred_water_time.sens$X.Length_cooking_time_in_s
pred_water_time.sens<-left_join(water_dat, pred_water_time.sens, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time.sens<- run_model(dry_dat.sens, ~ Length_cooking_time_in_s)
pred_dry_time.sens<-predict.rma(full_model_dry_time.sens, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time.sens<-as.data.frame(pred_dry_time.sens)
pred_dry_time.sens$Length_cooking_time_in_s=pred_dry_time.sens$X.Length_cooking_time_in_s
pred_dry_time.sens<-left_join(dry_dat.sens, pred_dry_time.sens, by="Length_cooking_time_in_s")
ggplot(dat.sens,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time.sens,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_time.sens,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time.sens,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))NA for the dry cooking categorydat.sens.vol <- filter(dat.sens, Ratio_liquid_fish != "NA")
VCV_lnRR.sens.vol <- make_VCV_matrix(dat.sens.vol, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens.vol <- rma.mv(lnRR, VCV_lnRR.sens.vol, mods = ~log(Ratio_liquid_fish), random = list(~1 |
Study_ID, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID), test = "t", data = dat.sens.vol)
summary(mod.sens.vol)##
## Multivariate Meta-Analysis Model (k = 406; method: REML)
##
## logLik Deviance AIC BIC AICc
## -347.9714 695.9428 707.9428 731.9513 708.1544
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.3245 0.5697 7 no Study_ID
## sigma^2.2 0.0018 0.0426 27 no Species_common
## sigma^2.3 0.1280 0.3578 19 no PFAS_type
## sigma^2.4 0.1571 0.3964 405 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 404) = 1747.6447, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 404) = 0.0923, p-val = 0.7614
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.1722 0.2411 -0.7144 404 0.4754 -0.6462 0.3017
## log(Ratio_liquid_fish) -0.0108 0.0356 -0.3038 404 0.7614 -0.0809 0.0592
##
## intrcpt
## log(Ratio_liquid_fish)
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat.sens.vol, mod.sens.vol, log(dat.sens.vol$Ratio_liquid_fish),
"ln(Liquid volume to tissue sample ratio (mL/g))") + scale_fill_manual(values = c("goldenrod2",
"dodgerblue3"))0 for the dry cooking categorydat.sens.vol0 <- filter(dat.sens, Ratio_liquid_fish_0 != "NA")
VCV_lnRR.sens.vol0 <- make_VCV_matrix(dat.sens.vol0, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens.vol0 <- rma.mv(lnRR, VCV_lnRR.sens.vol0, mods = ~log(Ratio_liquid_fish_0 +
1), random = list(~1 | Study_ID, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
test = "t", data = dat.sens.vol0)
summary(mod.sens.vol0)##
## Multivariate Meta-Analysis Model (k = 475; method: REML)
##
## logLik Deviance AIC BIC AICc
## -404.4430 808.8860 820.8860 845.8406 821.0663
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.3269 0.5717 7 no Study_ID
## sigma^2.2 0.0615 0.2481 27 no Species_common
## sigma^2.3 0.1288 0.3589 19 no PFAS_type
## sigma^2.4 0.1499 0.3872 474 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 473) = 2180.0767, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 473) = 2.7307, p-val = 0.0991
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.0980 0.2515 -0.3897 473 0.6969 -0.5922
## log(Ratio_liquid_fish_0 + 1) -0.0411 0.0249 -1.6525 473 0.0991 -0.0900
## ci.ub
## intrcpt 0.3962
## log(Ratio_liquid_fish_0 + 1) 0.0078 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat.sens.vol0, mod.sens.vol0, log(dat.sens.vol0$Ratio_liquid_fish_0 +
1), "ln(Liquid volume to tissue sample ratio + 1) (mL/g)") + scale_fill_manual(values = c("#55C667FF",
"goldenrod2", "dodgerblue3"))oil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
oil_dat_vol.sens <- filter(oil_dat.sens, Ratio_liquid_fish != "NA")
water_dat_vol.sens <- filter(water_dat.sens, Ratio_liquid_fish != "NA")
model_oil_vol.sens <- run_model(oil_dat_vol.sens, ~log(Ratio_liquid_fish))
model_water_vol.sens <- run_model(water_dat_vol.sens, ~log(Ratio_liquid_fish))
summary(model_oil_vol.sens)##
## Multivariate Meta-Analysis Model (k = 301; method: REML)
##
## logLik Deviance AIC BIC AICc
## -269.4112 538.8223 550.8223 573.0250 551.1100
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.4776 0.6911 6 no Study_ID
## sigma^2.2 0.0000 0.0000 24 no Species_common
## sigma^2.3 0.1039 0.3223 18 no PFAS_type
## sigma^2.4 0.1776 0.4215 301 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 299) = 1269.3583, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 299) = 0.0165, p-val = 0.8979
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.1844 0.3025 -0.6095 299 0.5427 -0.7797 0.4109
## log(Ratio_liquid_fish) 0.0057 0.0442 0.1284 299 0.8979 -0.0812 0.0926
##
## intrcpt
## log(Ratio_liquid_fish)
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_water_vol.sens)##
## Multivariate Meta-Analysis Model (k = 105; method: REML)
##
## logLik Deviance AIC BIC AICc
## -80.8643 161.7285 173.7285 189.5369 174.6035
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5477 0.7400 5 no Study_ID
## sigma^2.2 0.0000 0.0000 13 no Species_common
## sigma^2.3 0.1286 0.3586 16 no PFAS_type
## sigma^2.4 0.1110 0.3331 105 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 103) = 456.3043, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 103) = 6.2385, p-val = 0.0141
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.4849 0.5043 0.9616 103 0.3385 -0.5152
## log(Ratio_liquid_fish) -0.4471 0.1790 -2.4977 103 0.0141 -0.8021
## ci.ub
## intrcpt 1.4850
## log(Ratio_liquid_fish) -0.0921 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_vol.sens <- predict.rma(model_oil_vol.sens)
pred_water_vol.sens <- predict.rma(model_water_vol.sens)
oil_dat_vol.sens <- mutate(oil_dat_vol.sens, ci.lb = pred_oil_vol.sens$ci.lb, ci.ub = pred_oil_vol.sens$ci.ub,
fit = pred_oil_vol.sens$pred)
water_dat_vol.sens <- mutate(water_dat_vol.sens, ci.lb = pred_water_vol.sens$ci.lb,
ci.ub = pred_water_vol.sens$ci.ub, fit = pred_water_vol.sens$pred)For some reason the plot doesn’t want to knit, although the script works
ggplot(dat.sens, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = water_dat_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = water_dat_vol.sens, aes(y = fit), size = 1.5,
col = "dodgerblue") + col = "dodgerblue") +
geom_ribbon(data = oil_dat_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = oil_dat_vol.sens, aes(y = fit), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))##### Oil based
full_model_oil_vol.sens <- run_model(oil_dat.sens, ~scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
summary(full_model_oil_vol.sens)##
## Multivariate Meta-Analysis Model (k = 261; method: REML)
##
## logLik Deviance AIC BIC AICc
## -104.9549 209.9099 227.9099 259.8165 228.6416
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1405 0.3749 5 no Study_ID
## sigma^2.2 0.0186 0.1363 15 no Species_common
## sigma^2.3 0.1042 0.3228 17 no PFAS_type
## sigma^2.4 0.0270 0.1642 261 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 256) = 534.5877, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 256) = 27.8604, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.6283 0.2125 -2.9571 256 0.0034
## scale(Temperature_in_Celsius) -0.1098 0.1280 -0.8577 256 0.3919
## scale(Length_cooking_time_in_s) -0.3951 0.0389 -10.1583 256 <.0001
## scale(PFAS_carbon_chain) 0.1412 0.0705 2.0034 256 0.0462
## log_Ratio_liquid_fish -0.0664 0.0708 -0.9376 256 0.3493
## ci.lb ci.ub
## intrcpt -1.0468 -0.2099 **
## scale(Temperature_in_Celsius) -0.3619 0.1423
## scale(Length_cooking_time_in_s) -0.4717 -0.3185 ***
## scale(PFAS_carbon_chain) 0.0024 0.2800 *
## log_Ratio_liquid_fish -0.2057 0.0730
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_vol.sens <- predict.rma(full_model_oil_vol.sens, addx = TRUE, newmods = cbind(0,
0, 0, oil_dat.sens$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol.sens <- as.data.frame(pred_oil_vol.sens)
pred_oil_vol.sens <- pred_oil_vol.sens %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol.sens <- run_model(water_dat.sens, ~scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
summary(full_model_water_vol.sens)##
## Multivariate Meta-Analysis Model (k = 105; method: REML)
##
## logLik Deviance AIC BIC AICc
## -61.2148 122.4297 138.4297 159.3506 139.9949
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1609 0.4012 5 no Study_ID
## sigma^2.2 0.0000 0.0000 13 no Species_common
## sigma^2.3 0.1113 0.3336 16 no PFAS_type
## sigma^2.4 0.0617 0.2484 105 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 101) = 318.8891, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 101) = 15.8249, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt 0.0324 0.3616 0.0895 101 0.9289
## scale(Length_cooking_time_in_s) -0.4726 0.0761 -6.2118 101 <.0001
## scale(PFAS_carbon_chain) 0.1669 0.0786 2.1221 101 0.0363
## log_Ratio_liquid_fish -0.2496 0.1389 -1.7977 101 0.0752
## ci.lb ci.ub
## intrcpt -0.6850 0.7497
## scale(Length_cooking_time_in_s) -0.6235 -0.3217 ***
## scale(PFAS_carbon_chain) 0.0109 0.3228 *
## log_Ratio_liquid_fish -0.5251 0.0258 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_water_vol.sens <- predict.rma(full_model_water_vol.sens, addx = TRUE, newmods = cbind(0,
0, water_dat.sens$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol.sens <- as.data.frame(pred_water_vol.sens)
pred_water_vol.sens <- pred_water_vol.sens %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "water-based",
lnRR = 0)For some reason the plot doesn’t want to knit, although the script works
ggplot(dat.sens, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol.sens, aes(y = pred), size = 1.5,
col = "dodgerblue") + col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = pred_oil_vol.sens, aes(y = pred), size = 1.5,
col = "goldenrod") + col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2)) #### The line doesn't go all the way down (the predict function doesn't capture the biggest values)dat.sens.PFAS <- filter(dat.sens, PFAS_carbon_chain != "NA")
VCV_lnRR.sens.PFAS <- make_VCV_matrix(dat.sens.PFAS, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens.PFAS <- rma.mv(lnRR, VCV_lnRR.sens.PFAS, mods = ~PFAS_carbon_chain, random = list(~1 |
Study_ID, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID), test = "t", data = dat.sens.PFAS)
summary(mod.sens.PFAS)##
## Multivariate Meta-Analysis Model (k = 494; method: REML)
##
## logLik Deviance AIC BIC AICc
## -436.4061 872.8121 884.8121 910.0030 884.9853
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.2942 0.5424 9 no Study_ID
## sigma^2.2 0.0842 0.2901 31 no Species_common
## sigma^2.3 0.0896 0.2993 19 no PFAS_type
## sigma^2.4 0.1786 0.4226 493 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 492) = 3010.3986, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 492) = 1.2025, p-val = 0.2734
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.3214 0.3183 -1.0097 492 0.3132 -0.9467 0.3040
## PFAS_carbon_chain 0.0285 0.0260 1.0966 492 0.2734 -0.0226 0.0795
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat.sens.PFAS, mod.sens.PFAS, dat.sens.PFAS$PFAS_carbon_chain, "PFAS carbon chain length") # The relationship with cooking time appears even strongeroil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
dry_dat.sens <- filter(dat.sens, Cooking_Category == "No liquid")
oil_dat_PFAS.sens <- filter(oil_dat.sens, PFAS_carbon_chain != "NA")
water_dat_PFAS.sens <- filter(water_dat.sens, PFAS_carbon_chain != "NA")
dry_dat_PFAS.sens <- filter(dry_dat.sens, PFAS_carbon_chain != "NA")
model_oil_PFAS.sens <- run_model(oil_dat_PFAS.sens, ~PFAS_carbon_chain)
model_water_PFAS.sens <- run_model(water_dat_PFAS.sens, ~PFAS_carbon_chain)
model_dry_PFAS.sens <- run_model(dry_dat_PFAS.sens, ~PFAS_carbon_chain)
summary(model_oil_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 301; method: REML)
##
## logLik Deviance AIC BIC AICc
## -269.3590 538.7180 550.7180 572.9206 551.0057
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.4465 0.6682 6 no Study_ID
## sigma^2.2 0.0000 0.0000 24 no Species_common
## sigma^2.3 0.0945 0.3074 18 no PFAS_type
## sigma^2.4 0.1777 0.4216 301 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 299) = 1375.3149, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 299) = 1.5575, p-val = 0.2130
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.5083 0.3888 -1.3072 299 0.1921 -1.2735 0.2569
## PFAS_carbon_chain 0.0359 0.0288 1.2480 299 0.2130 -0.0207 0.0925
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_water_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 124; method: REML)
##
## logLik Deviance AIC BIC AICc
## -112.9082 225.8163 237.8163 254.6404 238.5467
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1975 0.4444 7 no Study_ID
## sigma^2.2 0.0158 0.1255 17 no Species_common
## sigma^2.3 0.0741 0.2721 16 no PFAS_type
## sigma^2.4 0.2134 0.4620 124 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 122) = 1144.5651, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 122) = 1.5694, p-val = 0.2127
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.5726 0.3533 -1.6208 122 0.1076 -1.2720 0.1268
## PFAS_carbon_chain 0.0422 0.0337 1.2527 122 0.2127 -0.0245 0.1089
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_dry_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 69; method: REML)
##
## logLik Deviance AIC BIC AICc
## -61.8104 123.6208 135.6208 148.8490 137.0208
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 1.1371 1.0664 2 no Study_ID
## sigma^2.2 0.0000 0.0000 14 no Species_common
## sigma^2.3 0.0620 0.2489 7 no PFAS_type
## sigma^2.4 0.1790 0.4231 69 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 67) = 302.7198, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 67) = 4.4625, p-val = 0.0384
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -1.4423 1.0193 -1.4150 67 0.1617 -3.4769 0.5922
## PFAS_carbon_chain 0.1558 0.0738 2.1125 67 0.0384 0.0086 0.3031 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_PFAS.sens <- predict.rma(model_oil_PFAS.sens)
pred_water_PFAS.sens <- predict.rma(model_water_PFAS.sens)
pred_dry_PFAS.sens <- predict.rma(model_dry_PFAS.sens)
oil_dat_PFAS.sens <- mutate(oil_dat_PFAS.sens, ci.lb = pred_oil_PFAS.sens$ci.lb,
ci.ub = pred_oil_PFAS.sens$ci.ub, fit = pred_oil_PFAS.sens$pred)
water_dat_PFAS.sens <- mutate(water_dat_PFAS.sens, ci.lb = pred_water_PFAS.sens$ci.lb,
ci.ub = pred_water_PFAS.sens$ci.ub, fit = pred_water_PFAS.sens$pred)
dry_dat_PFAS.sens <- mutate(dry_dat_PFAS.sens, ci.lb = pred_dry_PFAS.sens$ci.lb,
ci.ub = pred_dry_PFAS.sens$ci.ub, fit = pred_dry_PFAS.sens$pred)For some reason the plot doesn’t want to knit, although the script works
ggplot(dat.sens, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = dry_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = dry_dat_PFAS.sens, aes(y = fit), size = 1.5,
col = "palegreen3") + col = "palegreen3") +
geom_ribbon(data = oil_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = oil_dat_PFAS.sens, aes(y = fit), size = 1.5,
col = "goldenrod") + col = "goldenrod") +
geom_ribbon(data = water_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = water_dat_PFAS.sens, aes(y = fit), size = 1.5,
col = "dodgerblue") + col = "dodgerblue") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "PFAS carbon chain length",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
legend.direction = "horizontal", legend.title = element_text(size = 15),
panel.border = element_rect(colour = "black", fill = NA, size = 1.2))##### Oil based
full_model_oil_PFAS.sens<- run_model(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
summary(full_model_oil_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 261; method: REML)
##
## logLik Deviance AIC BIC AICc
## -104.9549 209.9099 227.9099 259.8165 228.6416
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1405 0.3749 5 no Study_ID
## sigma^2.2 0.0186 0.1363 15 no Species_common
## sigma^2.3 0.1042 0.3228 17 no PFAS_type
## sigma^2.4 0.0270 0.1642 261 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 256) = 534.5877, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 256) = 27.8604, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.1237 0.3350 -3.3541 256 0.0009
## scale(Temperature_in_Celsius) -0.1098 0.1280 -0.8577 256 0.3919
## scale(Length_cooking_time_in_s) -0.3951 0.0389 -10.1583 256 <.0001
## PFAS_carbon_chain 0.0574 0.0287 2.0034 256 0.0462
## scale(log(Ratio_liquid_fish)) -0.2153 0.2296 -0.9376 256 0.3493
## ci.lb ci.ub
## intrcpt -1.7835 -0.4640 ***
## scale(Temperature_in_Celsius) -0.3619 0.1423
## scale(Length_cooking_time_in_s) -0.4717 -0.3185 ***
## PFAS_carbon_chain 0.0010 0.1139 *
## scale(log(Ratio_liquid_fish)) -0.6673 0.2368
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_PFAS.sens<-predict.rma(full_model_oil_PFAS.sens, addx=TRUE, newmods=cbind(0,0, oil_dat.sens$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS.sens<-as.data.frame(pred_oil_PFAS.sens)
pred_oil_PFAS.sens$PFAS_carbon_chain=pred_oil_PFAS.sens$X.PFAS_carbon_chain
pred_oil_PFAS.sens<-left_join(oil_dat.sens, pred_oil_PFAS.sens, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS.sens<- run_model(water_dat.sens, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
summary(full_model_water_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 105; method: REML)
##
## logLik Deviance AIC BIC AICc
## -61.2148 122.4297 138.4297 159.3506 139.9949
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1609 0.4012 5 no Study_ID
## sigma^2.2 0.0000 0.0000 13 no Species_common
## sigma^2.3 0.1113 0.3336 16 no PFAS_type
## sigma^2.4 0.0617 0.2484 105 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 101) = 318.8891, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 101) = 15.8249, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.2316 0.3750 -3.2841 101 0.0014
## scale(Length_cooking_time_in_s) -0.4726 0.0761 -6.2118 101 <.0001
## PFAS_carbon_chain 0.0748 0.0353 2.1221 101 0.0363
## scale(log(Ratio_liquid_fish)) -0.2843 0.1581 -1.7977 101 0.0752
## ci.lb ci.ub
## intrcpt -1.9756 -0.4877 **
## scale(Length_cooking_time_in_s) -0.6235 -0.3217 ***
## PFAS_carbon_chain 0.0049 0.1448 *
## scale(log(Ratio_liquid_fish)) -0.5979 0.0294 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_water_PFAS.sens<-predict.rma(full_model_water_PFAS.sens, addx=TRUE, newmods=cbind(0, water_dat.sens$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS.sens<-as.data.frame(pred_water_PFAS.sens)
pred_water_PFAS.sens$PFAS_carbon_chain=pred_water_PFAS.sens$X.PFAS_carbon_chain
pred_water_PFAS.sens<-left_join(water_dat.sens, pred_water_PFAS.sens, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS.sens<- run_model(dry_dat.sens, ~ PFAS_carbon_chain)
pred_dry_PFAS.sens<-predict.rma(full_model_dry_PFAS.sens, addx=TRUE)
pred_dry_PFAS.sens<-as.data.frame(pred_dry_PFAS.sens)
pred_dry_PFAS.sens$PFAS_carbon_chain=pred_dry_PFAS.sens$X.PFAS_carbon_chain
pred_dry_PFAS.sens<-left_join(dry_dat.sens, pred_dry_PFAS.sens, by="PFAS_carbon_chain")
ggplot(dat.sens,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS.sens,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS.sens,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS.sens,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_mod.sens <- run_model(dat.sens, ~-1 + Cooking_Category + scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)))
funnel(full_mod.sens, yaxis = "seinv")full_model_time<- run_model(dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_full_model_time<-predict.rma(full_model_time, addx=TRUE, newmods=cbind(0,dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_time<-as.data.frame(pred_full_model_time)
pred_full_model_time$Length_cooking_time_in_s=pred_full_model_time$X.Length_cooking_time_in_s
pred_full_model_time<-left_join(dat, pred_full_model_time, by="Length_cooking_time_in_s")
uni_model_time<- run_model(dat, ~ Length_cooking_time_in_s)
pred_uni_model_time<-predict.rma(uni_model_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_time<-as.data.frame(pred_uni_model_time)
pred_uni_model_time$Length_cooking_time_in_s=pred_uni_model_time$X.Length_cooking_time_in_s
pred_uni_model_time<-left_join(dat, pred_uni_model_time, by="Length_cooking_time_in_s")
p_time<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_time,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_time,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_vol<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish)
pred_full_model_vol<-predict.rma(full_model_vol, addx=TRUE, newmods=cbind(0,0, 0, dat$log_Ratio_liquid_fish))
pred_full_model_vol<-as.data.frame(pred_full_model_vol)
pred_full_model_vol$log_Ratio_liquid_fish=pred_full_model_vol$X.log_Ratio_liquid_fish
pred_full_model_vol<- pred_full_model_vol %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), lnRR = 0)
uni_model_vol<- run_model(dat, ~ log_Ratio_liquid_fish)
pred_uni_model_vol<-predict.rma(uni_model_vol, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_vol<-as.data.frame(pred_uni_model_vol)
pred_uni_model_vol$log_Ratio_liquid_fish=pred_uni_model_vol$X.log_Ratio_liquid_fish
pred_uni_model_vol<- pred_uni_model_vol %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), lnRR = 0)
p_vol<-ggplot(dat,aes(x = log_Ratio_liquid_fish, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_vol,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_vol,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "ln (Liquid volume to tissue sample ratio) (mL/g)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_temp<- run_model(dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_full_model_temp<-predict.rma(full_model_temp, addx=TRUE, newmods=cbind(dat$Temperature_in_Celsius,0, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_temp<-as.data.frame(pred_full_model_temp)
pred_full_model_temp$Temperature_in_Celsius=pred_full_model_temp$X.Temperature_in_Celsius
pred_full_model_temp<-left_join(dat, pred_full_model_temp, by="Temperature_in_Celsius")
uni_model_temp<- run_model(dat, ~ Temperature_in_Celsius)
pred_uni_model_temp<-predict.rma(uni_model_temp, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_temp<-as.data.frame(pred_uni_model_temp)
pred_uni_model_temp$Temperature_in_Celsius=pred_uni_model_temp$X.Temperature_in_Celsius
pred_uni_model_temp<-left_join(dat, pred_uni_model_temp, by="Temperature_in_Celsius")
p_temp<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_temp,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_temp,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_PFAS<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_full_model_PFAS<-predict.rma(full_model_PFAS, addx=TRUE, newmods=cbind(0, 0, dat$PFAS_carbon_chain, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_PFAS<-as.data.frame(pred_full_model_PFAS)
pred_full_model_PFAS$PFAS_carbon_chain=pred_full_model_PFAS$X.PFAS_carbon_chain
pred_full_model_PFAS<-left_join(dat, pred_full_model_PFAS, by="PFAS_carbon_chain")
uni_model_PFAS<- run_model(dat, ~ PFAS_carbon_chain)
pred_uni_model_PFAS<-predict.rma(uni_model_PFAS, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_PFAS<-as.data.frame(pred_uni_model_PFAS)
pred_uni_model_PFAS$PFAS_carbon_chain=pred_uni_model_PFAS$X.PFAS_carbon_chain
pred_uni_model_PFAS<-left_join(dat, pred_uni_model_PFAS, by="PFAS_carbon_chain")
p_PFAS<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_PFAS,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_PFAS,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))(p_time + p_vol)/(p_temp + p_PFAS) + plot_annotation(tag_levels = c("A", "B", "C",
"D"))ggsave("fig/Fig_2.png", width = 15, height = 12, dpi = 1200)0 for the dry cooking methodfull_model_time0<- run_model(dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_full_model_time0<-predict.rma(full_model_time0, addx=TRUE, newmods=cbind(0,dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_time0<-as.data.frame(pred_full_model_time0)
pred_full_model_time0$Length_cooking_time_in_s=pred_full_model_time0$X.Length_cooking_time_in_s
pred_full_model_time0<-left_join(dat, pred_full_model_time0, by="Length_cooking_time_in_s")
uni_model_time<- run_model(dat, ~ Length_cooking_time_in_s)
pred_uni_model_time<-predict.rma(uni_model_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_time<-as.data.frame(pred_uni_model_time)
pred_uni_model_time$Length_cooking_time_in_s=pred_uni_model_time$X.Length_cooking_time_in_s
pred_uni_model_time<-left_join(dat, pred_uni_model_time, by="Length_cooking_time_in_s")
p_time0<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_time0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_time0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_time,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_vol0<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish0)
pred_full_model_vol0<-predict.rma(full_model_vol0, addx=TRUE, newmods=cbind(0,0, 0, dat$log_Ratio_liquid_fish0))
pred_full_model_vol0<-as.data.frame(pred_full_model_vol0)
pred_full_model_vol0$log_Ratio_liquid_fish0=pred_full_model_vol0$X.log_Ratio_liquid_fish
pred_full_model_vol0<- pred_full_model_vol0 %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish0)-1, lnRR = 0)
uni_model_vol0<- run_model(dat, ~ log_Ratio_liquid_fish0)
pred_uni_model_vol0<-predict.rma(uni_model_vol0, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_vol0<-as.data.frame(pred_uni_model_vol0)
pred_uni_model_vol0$log_Ratio_liquid_fish0=pred_uni_model_vol0$X.log_Ratio_liquid_fish
pred_uni_model_vol0<- pred_uni_model_vol0 %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish0) -1, lnRR = 0)
p_vol0<-ggplot(dat,aes(x = log_Ratio_liquid_fish0, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_vol0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_vol0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_vol0, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_vol0,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "ln (Liquid/animal tissue ratio + 1) (mL/g)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_temp0<- run_model(dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_full_model_temp0<-predict.rma(full_model_temp0, addx=TRUE, newmods=cbind(dat$Temperature_in_Celsius,0, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_temp0<-as.data.frame(pred_full_model_temp0)
pred_full_model_temp0$Temperature_in_Celsius=pred_full_model_temp0$X.Temperature_in_Celsius
pred_full_model_temp0<-left_join(dat, pred_full_model_temp0, by="Temperature_in_Celsius")
uni_model_temp<- run_model(dat, ~ Temperature_in_Celsius)
pred_uni_model_temp<-predict.rma(uni_model_temp, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_temp<-as.data.frame(pred_uni_model_temp)
pred_uni_model_temp$Temperature_in_Celsius=pred_uni_model_temp$X.Temperature_in_Celsius
pred_uni_model_temp<-left_join(dat, pred_uni_model_temp, by="Temperature_in_Celsius")
p_temp0<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_temp0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_temp0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_temp,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_PFAS0<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_full_model_PFAS0<-predict.rma(full_model_PFAS0, addx=TRUE, newmods=cbind(0, 0, dat$PFAS_carbon_chain, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_PFAS0<-as.data.frame(pred_full_model_PFAS0)
pred_full_model_PFAS0$PFAS_carbon_chain=pred_full_model_PFAS0$X.PFAS_carbon_chain
pred_full_model_PFAS0<-left_join(dat, pred_full_model_PFAS0, by="PFAS_carbon_chain")
uni_model_PFAS<- run_model(dat, ~ PFAS_carbon_chain)
pred_uni_model_PFAS<-predict.rma(uni_model_PFAS, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_PFAS<-as.data.frame(pred_uni_model_PFAS)
pred_uni_model_PFAS$PFAS_carbon_chain=pred_uni_model_PFAS$X.PFAS_carbon_chain
pred_uni_model_PFAS<-left_join(dat, pred_uni_model_PFAS, by="PFAS_carbon_chain")
p_PFAS0<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_PFAS0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_PFAS0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_PFAS,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))(p_time0 + p_vol0)/(p_temp0 + p_PFAS0) + plot_annotation(tag_levels = c("A", "B",
"C", "D"))ggsave("fig/Fig_2_zero_ratio.png", width = 15, height = 12, dpi = 1200)my_orchard<- function (object, mod = "1", group, data, xlab, N = "none",
alpha = 0.5, angle = 90, cb = FALSE, k = TRUE, g = TRUE,
trunk.size = 3, branch.size = 1.2, twig.size = 0.5, transfm = c("none",
"tanh"), condition.lab = "Condition", legend.pos = c("bottom.right",
"bottom.left", "top.right", "top.left",
"top.out", "bottom.out"), k.pos = c("right",
"left"), weights = "prop", by = NULL, at = NULL)
{
transfm <- match.arg(NULL, choices = transfm)
legend.pos <- match.arg(NULL, choices = legend.pos)
k.pos <- match.arg(NULL, choices = k.pos)
if (any(class(object) %in% c("rma.mv", "rma"))) {
if (mod != "1") {
results <- orchaRd::mod_results(object, mod, group,
data, by = by, at = at, weights = weights)
}
else {
results <- orchaRd::mod_results(object, mod = "1",
group, data, by = by, at = at, weights = weights)
}
}
if (any(class(object) %in% c("orchard"))) {
results <- object
}
mod_table <- results$mod_table
data_trim <- results$data
data_trim$moderator <- factor(data_trim$moderator, levels = mod_table$name,
labels = mod_table$name)
data_trim$scale <- (1/sqrt(data_trim[, "vi"]))
legend <- "Precision (1/SE)"
if (any(N != "none")) {
data_trim$scale <- N
legend <- paste0("Sample Size (", "N", ")")
}
if (transfm == "tanh") {
cols <- sapply(mod_table, is.numeric)
mod_table[, cols] <- Zr_to_r(mod_table[, cols])
data_trim$yi <- Zr_to_r(data_trim$yi)
label <- xlab
}
else {
label <- xlab
}
mod_table$K <- as.vector(by(data_trim, data_trim[, "moderator"],
function(x) length(x[, "yi"])))
mod_table$g <- as.vector(num_studies(data_trim, moderator,
stdy)[, 2])
group_no <- length(unique(mod_table[, "name"]))
cbpl <- c("#88CCEE", "#CC6677", "#DDCC77",
"#117733", "#332288", "#AA4499", "#44AA99",
"#999933", "#882255", "#661100", "#6699CC",
"#888888", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7",
"#999999")
if (names(mod_table)[2] == "condition") {
condition_no <- length(unique(mod_table[, "condition"]))
plot <- ggplot2::ggplot() + ggbeeswarm::geom_quasirandom(data = data_trim,
ggplot2::aes(y = yi, x = moderator, size = scale,
colour = moderator), alpha = alpha) + ggplot2::geom_hline(yintercept = 0,
linetype = 2, colour = "black", alpha = alpha) +
ggplot2::geom_linerange(data = mod_table, ggplot2::aes(x = name,
ymin = lowerCL, ymax = upperCL), size = branch.size,
position = ggplot2::position_dodge2(width = 0.3)) +
ggplot2::geom_pointrange(data = mod_table, ggplot2::aes(y = estimate,
x = name, ymin = lowerPR, ymax = upperPR, shape = as.factor(condition),
fill = name), size = twig.size, stroke=2.2,position = ggplot2::position_dodge2(width = 0.3), # Added stroke
fatten = trunk.size) + ggplot2::scale_shape_manual(values = 20 +
(1:condition_no)) + ggplot2::coord_flip() + ggplot2::theme_bw() +
ggplot2::guides(fill = "none", colour = "none") +
ggplot2::theme(legend.position = c(0, 1), legend.justification = c(0,
1)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) +
ggplot2::theme(legend.direction = "horizontal") +
ggplot2::theme(legend.background = ggplot2::element_blank()) +
ggplot2::labs(y = label, x = "", size = legend,
parse = TRUE) + ggplot2::labs(shape = condition.lab) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10,
colour = "black", hjust = 0.5, angle = angle))
}
else {
plot <- ggplot2::ggplot() + ggbeeswarm::geom_quasirandom(data = data_trim,
ggplot2::aes(y = yi, x = moderator, size = scale,
colour = moderator), alpha = alpha) + ggplot2::geom_hline(yintercept = 0,
linetype = 2, colour = "black", alpha = alpha) +
ggplot2::geom_linerange(data = mod_table, ggplot2::aes(x = name,
ymin = lowerCL, ymax = upperCL), size = branch.size) +
ggplot2::geom_pointrange(data = mod_table, ggplot2::aes(y = estimate,
x = name, ymin = lowerPR, ymax = upperPR, fill = name),
size = twig.size, fatten = trunk.size, shape = 21) +
ggplot2::coord_flip() + ggplot2::theme_bw() + ggplot2::guides(fill = "none",
colour = "none") + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) +
ggplot2::theme(legend.direction = "horizontal") +
ggplot2::theme(legend.background = ggplot2::element_blank()) +
ggplot2::labs(y = label, x = "", size = legend) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10,
colour = "black", hjust = 0.5, angle = angle))
}
if (legend.pos == "bottom.right") {
plot <- plot + ggplot2::theme(legend.position = c(1,
0), legend.justification = c(1, 0))
}
else if (legend.pos == "bottom.left") {
plot <- plot + ggplot2::theme(legend.position = c(0,
0), legend.justification = c(0, 0))
}
else if (legend.pos == "top.right") {
plot <- plot + ggplot2::theme(legend.position = c(1,
1), legend.justification = c(1, 1))
}
else if (legend.pos == "top.left") {
plot <- plot + ggplot2::theme(legend.position = c(0,
1), legend.justification = c(0, 1))
}
else if (legend.pos == "top.out") {
plot <- plot + ggplot2::theme(legend.position = "top")
}
else if (legend.pos == "bottom.out") {
plot <- plot + ggplot2::theme(legend.position = "bottom")
}
if (cb == TRUE) {
plot <- plot + ggplot2::scale_fill_manual(values = cbpl) +
ggplot2::scale_colour_manual(values = cbpl)
}
if (k == TRUE && g == FALSE && k.pos == "right") {
plot <- plot + ggplot2::annotate("text", y = (max(data_trim$yi) +
(max(data_trim$yi) * 0.1)), x = (seq(1, group_no,
1) + 0.3), label = paste("italic(k)==", mod_table$K[1:group_no]),
parse = TRUE, hjust = "right", size = 3.5)
}
else if (k == TRUE && g == FALSE && k.pos == "left") {
plot <- plot + ggplot2::annotate("text", y = (min(data_trim$yi) +
(min(data_trim$yi) * 0.1)), x = (seq(1, group_no,
1) + 0.3), label = paste("italic(k)==", mod_table$K[1:group_no]),
parse = TRUE, hjust = "left", size = 3.5)
}
else if (k == TRUE && g == TRUE && k.pos == "right") {
plot <- plot + ggplot2::annotate("text", y = (max(data_trim$yi) +
(max(data_trim$yi) * 0.1)), x = (seq(1, group_no,
1) + 0.3), label = paste("italic(k)==", mod_table$K[1:group_no],
" (", mod_table$g[1:group_no], ")"),
parse = TRUE, hjust = "right", size = 3.5)
}
else if (k == TRUE && g == TRUE && k.pos == "left") {
plot <- plot + ggplot2::annotate("text", y = (min(data_trim$yi) +
(min(data_trim$yi) * 0.1)), x = (seq(1, group_no,
1) + 0.3), label = paste("italic(k)==", mod_table$K[1:group_no],
" (", mod_table$g[1:group_no], ")"),
parse = TRUE, hjust = "left", size = 3.5)
}
return(plot)
}full_model_org_units <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)
# full model with Ratio_liquid_fish taken as `0` for the dry cooking category
full_model_org_units0 <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish0)
# full model without the 'No liquid' data for figure 3B, when Ratio_liquid_fish
# is taken as `NA` for the dry cooking category
full_model_org_units_oil_water <- run_model(dat_oil_water, ~-1 + Cooking_Category +
Temperature_in_Celsius + Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)NA for the dry cooking categoryEstimates at cooking times of 2, 10 and 25 min
time_mm <-mod_results(full_model_org_units, data = dat, mod = "1", group="Study_ID", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm<-my_orchard(time_mm, xlab = "lnRR", mod="1", condition.lab = "Cooking time (sec)", group="Study_ID", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10))+
guides(size=F)Estimates at 0 mL/g of tissue, 10 mL/g of tissue or 45 mL/g of tissue
volume_mm <-mod_results(full_model_org_units_oil_water, data = dat_oil_water, mod = "1", group="Study_ID",at = list(log_Ratio_liquid_fish= c(-2.3, 2.3, 3.8)), by = "log_Ratio_liquid_fish")
p_volume_mm<-my_orchard(volume_mm, xlab = "lnRR", condition.lab = "ln (Liquid to sample ratio)", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10))+
guides(size=F)Estimates at cooking times of 2, 10 and 25 min
time_mm_cat <- mod_results(full_model_org_units, data = dat, mod = "Cooking_Category", group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_cat<-my_orchard(time_mm_cat, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 10), breaks=c(2,4,6))+
scale_fill_manual(values=c("goldenrod2", "dodgerblue3"))+
scale_colour_manual(values = c("goldenrod2", "dodgerblue3"))+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10))((p_time_mm/p_volume_mm) | p_time_mm_cat) + plot_annotation(tag_levels = c("A", "B",
"C"))ggsave("fig/Fig_3.png", width = 14, height = 10, dpi = 1200)0 for the dry cooking categoryEstimates at cooking times of 2, 10 and 25 min
time_mm0 <-mod_results(full_model_org_units0, data = dat, mod = "1", group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm0<-my_orchard(time_mm0, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left", k=F)+
scale_size_continuous(range = c(1, 10), breaks=c(2,4,6))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10))+
ylim(-6.05, 3)+
annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 439), parse = TRUE, hjust = "right", size = 3.5) +
annotate("text", y = 2.3, x = 1.299, label = paste("(7)"), parse = TRUE, hjust = "right", size = 3.5) Estimates at 0.1 mL/g of tissue, 10 mL/g of tissue or 45 mL/g of tissue
volume_mm0 <-mod_results(full_model_org_units0, data = dat, mod = "1", group="Study_ID",at = list(log_Ratio_liquid_fish0= c(0, 2.4, 3.8)), by = "log_Ratio_liquid_fish0")
p_volume_mm0<-my_orchard(volume_mm0, xlab = "lnRR", condition.lab = "ln (Liquid/animal tissue ratio + 1) (mL/g)", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left", k=F)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10))+
guides(size=F)+
ylim(-6.05, 3)+
annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 439), parse = TRUE, hjust = "right", size = 3.5) +
annotate("text", y = 2.3, x = 1.297, label = paste("(7)"), parse = TRUE, hjust = "right", size = 3.5) Estimates at cooking times of 2, 10 and 25 min
In this case, water- and oil-based cooking must be separated from dry cooking to avoid extrapolations of the dry cooking effect sizes at the mean liquid ratio.
time_mm_cat <- mod_results(full_model_org_units, data = dat, mod = "Cooking_Category", group="Study_ID", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_wat_oil<-my_orchard(time_mm_cat ,xlab = "lnRR", group="Study_ID", condition.lab = "Cooking time (sec)", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left", k=F)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values=c("goldenrod2", "dodgerblue3"))+
scale_colour_manual(values = c("goldenrod2", "dodgerblue3"))+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 0), # change font sizes
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.position = "none")+
guides(shape=F, size=F)+
ylim(-6.1, 3)+
annotate("text", y = 1.9, x = (seq(1, 2, 1) + 0.301), label = paste("italic(k)==", c(267, 125)), parse = TRUE, hjust = "right", size = 3.5)+
annotate("text", y = 2.3, x = (seq(1, 2, 1) + 0.3), label = paste(c("(6)", "(6)")), parse = TRUE, hjust = "right", size = 3.5)
time_mm_dry<-mod_results(full_model_org_units_dry, data = dat, group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_dry<-my_orchard(time_mm_dry, xlab = "lnRR", group="Study_ID", condition.lab = "Cooking time (sec)", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left", k=F)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values=c("#55C667FF"))+
scale_colour_manual(values = c("#55C667FF"))+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.margin=margin(1,1,1,1))+
guides(size=F)+
ylim(-6.05, 3)+
annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 47), parse = TRUE, hjust = "right", size = 3.5)+
annotate("text", y = 2.3, x = 1.299, label = paste("(3)"), parse = TRUE, hjust = "right", size = 3.5)
p_time_mm_cat<-p_time_mm_wat_oil/p_time_mm_dry + plot_layout(heights=c(2,1))((p_time_mm0/p_volume_mm0) | p_time_mm_cat) + plot_annotation(tag_levels = c("A",
"B", "C"))ggsave("fig/Fig_3_zero_ratio.png", width = 14, height = 11, dpi = 1200)NA for the dry cooking category##### Oil based
full_model_oil_time<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_time<-predict.rma(full_model_oil_time, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time<-as.data.frame(pred_oil_time)
pred_oil_time$Length_cooking_time_in_s=pred_oil_time$X.Length_cooking_time_in_s
pred_oil_time<-left_join(oil_dat, pred_oil_time, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_water_time<-predict.rma(full_model_water_time, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time<-as.data.frame(pred_water_time)
pred_water_time$Length_cooking_time_in_s=pred_water_time$X.Length_cooking_time_in_s
pred_water_time<-left_join(water_dat, pred_water_time, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
p_4A<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(fill=F)##### Oil based
full_model_oil_vol <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_oil_vol <- predict.rma(full_model_oil_vol, addx = TRUE, newmods = cbind(0, 0,
0, oil_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol <- as.data.frame(pred_oil_vol)
pred_oil_vol <- pred_oil_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol <- run_model(water_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_water_vol <- predict.rma(full_model_water_vol, addx = TRUE, newmods = cbind(0,
0, water_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol <- as.data.frame(pred_water_vol)
pred_water_vol <- pred_water_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "water-based",
lnRR = 0)
p_4B <- ggplot(dat, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol, aes(y = pred), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data = pred_oil_vol, aes(y = pred), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio) (mL/g)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = "none", panel.border = element_rect(colour = "black", fill = NA,
size = 1.2)) #### The line doesn't go all the way down for water-based because the highest values are not included in the full modelfull_model_oil_temp<- run_model(oil_dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_temp<-predict.rma(full_model_oil_temp, addx=TRUE, newmods=cbind(oil_dat$Temperature_in_Celsius,0, 0,0))
pred_oil_temp<-as.data.frame(pred_oil_temp)
pred_oil_temp$Temperature_in_Celsius=pred_oil_temp$X.Temperature_in_Celsius
pred_oil_temp<-left_join(oil_dat, pred_oil_temp, by="Temperature_in_Celsius")
p_4C<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_oil_temp, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_temp,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(size=F)##### Oil based
full_model_oil_PFAS<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_oil_PFAS<-predict.rma(full_model_oil_PFAS, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS<-as.data.frame(pred_oil_PFAS)
pred_oil_PFAS$PFAS_carbon_chain=pred_oil_PFAS$X.PFAS_carbon_chain
pred_oil_PFAS<-left_join(oil_dat, pred_oil_PFAS, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_water_PFAS<-predict.rma(full_model_water_PFAS, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS<-as.data.frame(pred_water_PFAS)
pred_water_PFAS$PFAS_carbon_chain=pred_water_PFAS$X.PFAS_carbon_chain
pred_water_PFAS<-left_join(water_dat, pred_water_PFAS, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
p_4D<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))(p_4A + p_4B)/(p_4C + p_4D) + plot_annotation(tag_levels = c("A", "B", "C", "D"))ggsave("fig/Fig_4.png", width = 15, height = 12, dpi = 1200)0 for the dry cooking category##### Oil based
full_model_oil_time0<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_time0<-predict.rma(full_model_oil_time0, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time0<-as.data.frame(pred_oil_time0)
pred_oil_time0$Length_cooking_time_in_s=pred_oil_time0$X.Length_cooking_time_in_s
pred_oil_time0<-left_join(oil_dat, pred_oil_time0, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time0<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_water_time0<-predict.rma(full_model_water_time0, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time0<-as.data.frame(pred_water_time0)
pred_water_time0$Length_cooking_time_in_s=pred_water_time0$X.Length_cooking_time_in_s
pred_water_time0<-left_join(water_dat, pred_water_time0, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
p_4A0<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(fill=F)##### Oil based
full_model_oil_vol0 <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish0)
pred_oil_vol0 <- predict.rma(full_model_oil_vol0, addx = TRUE, newmods = cbind(0,
0, 0, oil_dat$log_Ratio_liquid_fish0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol0 <- as.data.frame(pred_oil_vol0)
pred_oil_vol0 <- pred_oil_vol0 %>%
mutate(Ratio_liquid_fish_0 = exp(X.log_Ratio_liquid_fish0) - 1, Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol0 <- run_model(water_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish0)
pred_water_vol0 <- predict.rma(full_model_water_vol0, addx = TRUE, newmods = cbind(0,
0, water_dat$log_Ratio_liquid_fish0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol0 <- as.data.frame(pred_water_vol0)
pred_water_vol0 <- pred_water_vol0 %>%
mutate(Ratio_liquid_fish_0 = exp(X.log_Ratio_liquid_fish0) - 1, Cooking_Category = "water-based",
lnRR = 0)
p_4B0 <- ggplot(dat, aes(x = log(Ratio_liquid_fish_0 + 1), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol0, aes(y = pred), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = pred_oil_vol0, aes(y = pred), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid/animal tissue ratio + 1) (mL/g)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = "none", panel.border = element_rect(colour = "black", fill = NA,
size = 1.2))full_model_oil_temp0<- run_model(oil_dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_temp0<-predict.rma(full_model_oil_temp0, addx=TRUE, newmods=cbind(oil_dat$Temperature_in_Celsius,0, 0,0))
pred_oil_temp0<-as.data.frame(pred_oil_temp0)
pred_oil_temp0$Temperature_in_Celsius=pred_oil_temp0$X.Temperature_in_Celsius
pred_oil_temp0<-left_join(oil_dat, pred_oil_temp0, by="Temperature_in_Celsius")
p_4C0<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_oil_temp0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_temp0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"),
labels=c("no liquid", "oil-based", "water-based"))+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(size=F)##### Oil based
full_model_oil_PFAS0<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_PFAS0<-predict.rma(full_model_oil_PFAS0, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS0<-as.data.frame(pred_oil_PFAS0)
pred_oil_PFAS0$PFAS_carbon_chain=pred_oil_PFAS0$X.PFAS_carbon_chain
pred_oil_PFAS0<-left_join(oil_dat, pred_oil_PFAS0, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS0<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_water_PFAS0<-predict.rma(full_model_water_PFAS0, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS0<-as.data.frame(pred_water_PFAS0)
pred_water_PFAS0$PFAS_carbon_chain=pred_water_PFAS0$X.PFAS_carbon_chain
pred_water_PFAS0<-left_join(water_dat, pred_water_PFAS0, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
p_4D0<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))(p_4A0 + p_4B0)/(p_4C0 + p_4D0) + plot_annotation(tag_levels = c("A", "B", "C", "D"))ggsave("fig/Fig_4_zero_ratio.png", width = 15, height = 12, dpi = 1200)NA for the dry cooking categorydat$Study_ID<- as.factor(dat$Study_ID)
# funnel(full_model,
# yaxis="seinv", # Inverse of standard error (precision) as the y axis
# level = c(90, 95, 99), # levels of statistical significance highlighted
# shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
# legend = TRUE, # display legend
# ylab="Precision (1/SE)",
# cex.lab=1.75,
# digits=1,
# cex=2,
# pch=21,
# col=dat$Study_ID)
pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,6,0.1,0))
plot_f <- funnel(full_model,
yaxis="seinv", # Inverse of standard error (precision) as the y axis
level = c(90, 95, 99), # levels of statistical significance highlighted
shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
legend = TRUE, # display legend
ylab="Precision (1/SE)",
cex.lab=1.75,
digits=1,
ylim=c(0.82,0.94),
xlim=c(-6, 6),
cex=2,
pch=21,
col=dat$Study_ID)p_5A <- recordPlot(plot_f)
invisible(dev.off())full_model_egger <- run_model(dat, ~ - 1 +
I(sqrt(1/N_tilde)) +
scale(Publication_year) +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish))) # Model to get predictions
pred_egger<-predict.rma(full_model_egger, addx=TRUE, newmods=cbind(sqrt(1/dat$N_tilde),0,0,0 ,0, 0))
pred_egger<-as.data.frame(pred_egger)
pred_egger$SE_eff_N=pred_egger$X.I.sqrt.1.N_tilde..
pred_egger<- pred_egger %>% mutate(N_tilde = ((1/X.I.sqrt.1.N_tilde..)^2), lnRR = 0)
p_5B<-ggplot(dat,aes(x = sqrt(1/N_tilde), y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_egger, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_egger,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Standard error", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
xlim(0.18,1)full_model_pub <- run_model(dat, ~ - 1 +
scale(I(sqrt(1/N_tilde))) +
Publication_year +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish))) # Model to get predictions
pred_pub<-predict.rma(full_model_pub, addx=TRUE, newmods=cbind(0,dat$Publication_year,0,0 ,0, 0))
pred_pub<-as.data.frame(pred_pub)
pred_pub$Publication_year=pred_pub$X.Publication_year
pred_pub<-left_join(dat, pred_pub, by="Publication_year")
p_5C<-ggplot(dat,aes(x = Publication_year, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_pub, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_pub,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Publication year", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2)) +
scale_x_continuous(breaks=c(2008, 2010, 2012, 2014, 2016, 2018 ,2020))(ggdraw(p_5A) + ggdraw(p_5B) + ggdraw(p_5C) + plot_annotation(tag_levels = "A"))ggsave(here("fig/Fig_5BC.png"), width = 18, height = 7, dpi = 1200)0 for the dry cooking categorydat$Study_ID<- as.factor(dat$Study_ID)
# funnel(full_model,
# yaxis="seinv", # Inverse of standard error (precision) as the y axis
# level = c(90, 95, 99), # levels of statistical significance highlighted
# shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
# legend = TRUE, # display legend
# ylab="Precision (1/SE)",
# cex.lab=1.75,
# digits=1,
# cex=2,
# pch=21,
# col=dat$Study_ID)
pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,6,0.1,0))
plot_f0 <- funnel(full_model0,
yaxis="seinv", # Inverse of standard error (precision) as the y axis
level = c(90, 95, 99), # levels of statistical significance highlighted
shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
legend = TRUE, # display legend
ylab="Precision (1/SE)",
cex.lab=1.75,
digits=1,
ylim=c(0.82,0.94),
xlim=c(-6, 6),
cex=2,
pch=21,
col=dat$Study_ID)p_5A0 <- recordPlot(plot_f0)
invisible(dev.off())full_model_egger0 <- run_model(dat, ~ - 1 +
I(sqrt(1/N_tilde)) +
scale(Publication_year) +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1))) # Model to get predictions
pred_egger0<-predict.rma(full_model_egger0, addx=TRUE, newmods=cbind(sqrt(1/dat$N_tilde),0,0,0 ,0, 0))
pred_egger0<-as.data.frame(pred_egger0)
pred_egger0$SE_eff_N=pred_egger0$X.I.sqrt.1.N_tilde..
pred_egger0<- pred_egger0 %>% mutate(N_tilde = ((1/X.I.sqrt.1.N_tilde..)^2), lnRR = 0)
p_5B0<-ggplot(dat,aes(x = sqrt(1/N_tilde), y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_egger0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_egger0,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Standard error", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
xlim(0.18,1)full_model_pub0 <- run_model(dat, ~ - 1 +
scale(I(sqrt(1/N_tilde))) +
Publication_year +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1))) # Model to get predictions
pred_pub0<-predict.rma(full_model_pub0, addx=TRUE, newmods=cbind(0,dat$Publication_year,0,0 ,0, 0))
pred_pub0<-as.data.frame(pred_pub0)
pred_pub0$Publication_year=pred_pub0$X.Publication_year
pred_pub0<-left_join(dat, pred_pub0, by="Publication_year")
p_5C0<-ggplot(dat,aes(x = Publication_year, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_pub0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_pub0,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Publication year", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2)) +
scale_x_continuous(breaks=c(2008, 2010, 2012, 2014, 2016, 2018 ,2020))(ggdraw(p_5A0) + ggdraw(p_5B0) + ggdraw(p_5C0) + plot_annotation(tag_levels = "A"))ggsave(here("fig/Fig_5BC_zero_ratio.png"), width = 18, height = 7, dpi = 1200)sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252
## [3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C
## [5] LC_TIME=English_Australia.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggstatsplot_0.8.0 cowplot_1.1.1 GGally_2.1.2
## [4] kableExtra_1.3.4 emmeans_1.7.2-9000003 patchwork_1.1.1
## [7] clubSandwich_0.5.3 ape_5.5 orchaRd_2.0
## [10] metaAidR_0.0.0.9000 metafor_3.0-2 Matrix_1.3-4
## [13] here_1.0.1 googlesheets4_1.0.0 forcats_0.5.1
## [16] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
## [19] readr_2.1.1 tidyr_1.1.3 tibble_3.1.3
## [22] ggplot2_3.3.5 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 pairwiseComparisons_3.1.6
## [3] backports_1.4.1 systemfonts_1.0.2
## [5] plyr_1.8.6 splines_4.1.0
## [7] gmp_0.6-2 kSamples_1.2-9
## [9] ipmisc_6.0.2 TH.data_1.0-10
## [11] digest_0.6.27 SuppDists_1.1-9.5
## [13] htmltools_0.5.1.1 fansi_0.5.0
## [15] magrittr_2.0.1 memoise_2.0.0
## [17] paletteer_1.4.0 tzdb_0.2.0
## [19] modelr_0.1.8 sandwich_3.0-1
## [21] svglite_2.0.0 rmdformats_1.0.2
## [23] colorspace_2.0-2 rvest_1.0.2
## [25] ggrepel_0.9.1 haven_2.4.3
## [27] xfun_0.29 crayon_1.4.2
## [29] jsonlite_1.7.2 zeallot_0.1.0
## [31] survival_3.2-11 zoo_1.8-9
## [33] glue_1.4.2 gtable_0.3.0
## [35] gargle_1.2.0 webshot_0.5.2
## [37] MatrixModels_0.5-0 statsExpressions_1.1.0
## [39] Rmpfr_0.8-4 scales_1.1.1
## [41] mvtnorm_1.1-3 DBI_1.1.2
## [43] PMCMRplus_1.9.0 Rcpp_1.0.7
## [45] viridisLite_0.4.0 xtable_1.8-4
## [47] performance_0.7.3 datawizard_0.2.0.1
## [49] httr_1.4.2 RColorBrewer_1.1-2
## [51] ellipsis_0.3.2 pkgconfig_2.0.3
## [53] reshape_0.8.8 farver_2.1.0
## [55] multcompView_0.1-8 sass_0.4.0
## [57] dbplyr_2.1.1 utf8_1.2.2
## [59] tidyselect_1.1.1 labeling_0.4.2
## [61] rlang_0.4.11 effectsize_0.4.5
## [63] munsell_0.5.0 cellranger_1.1.0
## [65] tools_4.1.0 cachem_1.0.5
## [67] cli_3.0.1 generics_0.1.1
## [69] broom_0.7.11 mathjaxr_1.4-0
## [71] evaluate_0.14 fastmap_1.1.0
## [73] BWStest_0.2.2 yaml_2.2.1
## [75] rematch2_2.1.2 knitr_1.37
## [77] fs_1.5.0 WRS2_1.1-3
## [79] pbapply_1.5-0 nlme_3.1-152
## [81] formatR_1.11 xml2_1.3.3
## [83] correlation_0.7.0 compiler_4.1.0
## [85] rstudioapi_0.13 beeswarm_0.4.0
## [87] ggsignif_0.6.2 reprex_2.0.1
## [89] bslib_0.2.5.1 stringi_1.7.6
## [91] highr_0.9 parameters_0.14.0
## [93] lattice_0.20-44 vctrs_0.3.8
## [95] pillar_1.6.5 lifecycle_1.0.1
## [97] mc2d_0.1-21 jquerylib_0.1.4
## [99] estimability_1.3 insight_0.14.2
## [101] R6_2.5.1 bookdown_0.22
## [103] vipor_0.4.5 BayesFactor_0.9.12-4.2
## [105] codetools_0.2-18 MASS_7.3-54
## [107] gtools_3.9.2 assertthat_0.2.1
## [109] rprojroot_2.0.2 withr_2.4.3
## [111] multcomp_1.4-17 bayestestR_0.10.5
## [113] parallel_4.1.0 hms_1.1.1
## [115] grid_4.1.0 coda_0.19-4
## [117] rmarkdown_2.11 googledrive_2.0.0
## [119] lubridate_1.8.0 ggbeeswarm_0.6.0